Yue Zhang


2024

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NavHint: Vision and Language Navigation Agent with a Hint Generator
Yue Zhang | Quan Guo | Parisa Kordjamshidi
Findings of the Association for Computational Linguistics: EACL 2024

The existing work on vision and language navigation mainly relies on navigation-related losses to establish the connection between vision and language modalities, neglecting aspects of helping the navigation agent build a deep understanding of the visual environment.In our work, we provide indirect supervision to the navigation agent through a hint generator that provides detailed visual descriptions.The hint generator assists the navigation agent in developing a global understanding of the visual environment. It directs the agent’s attention toward related navigation details, including the relevant sub-instruction, potential challenges in recognition and ambiguities in grounding, and the targeted viewpoint description. To train the hint generator, we construct a synthetic dataset based on landmarks in the instructions and visible and distinctive objects in the visual environment.We evaluate our method on the R2R and R4R datasets and achieve state-of-the-art on several metrics. The experimental results demonstrate that generating hints not only enhances the navigation performance but also helps improve the agent’s interpretability.

2023

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Learning to Generalize for Cross-domain QA
Yingjie Niu | Linyi Yang | Ruihai Dong | Yue Zhang
Findings of the Association for Computational Linguistics: ACL 2023

There have been growing concerns regarding the out-of-domain generalization ability of natural language processing (NLP) models, particularly in question-answering (QA) tasks. Current synthesized data augmentation methods for QA are hampered by increased training costs. To address this issue, we propose a novel approach that combines prompting methods and linear probing with fine-tuning strategy, which does not entail additional cost. Our method has been theoretically and empirically shown to be effective in enhancing the generalization ability of both generative and discriminative models. Our approach outperforms state-of-the-art baselines, with an average increase in F1 score of 4.5%-7.9%. Furthermore, our method can be easily integrated into any pre-trained models and offers a promising solution to the under-explored cross-domain QA task.

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Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis
Chenyang Lyu | Linyi Yang | Yue Zhang | Yvette Graham | Jennifer Foster
Findings of the Association for Computational Linguistics: ACL 2023

User and product information associated with a review is useful for sentiment polarity prediction. Typical approaches incorporating such information focus on modeling users and products as implicitly learned representation vectors. Most do not exploit the potential of historical reviews, or those that currently do require unnecessary modifications to model architectureor do not make full use of user/product associations. The contribution of this work is twofold: i) a method to explicitly employ historical reviews belonging to the same user/product in initializing representations, and ii) efficient incorporation of textual associations between users and products via a user-product cross-context module. Experiments on the IMDb, Yelp-2013 and Yelp-2014 English benchmarks with BERT, SpanBERT and Longformer pretrained language models show that our approach substantially outperforms previous state-of-the-art.

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Exploiting Abstract Meaning Representation for Open-Domain Question Answering
Cunxiang Wang | Zhikun Xu | Qipeng Guo | Xiangkun Hu | Xuefeng Bai | Zheng Zhang | Yue Zhang
Findings of the Association for Computational Linguistics: ACL 2023

The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database. Current systems leverage Pretrained Language Models (PLMs) to model the relationship between questions and passages. However, the diversity in surface form expressions can hinder the model’s ability to capture accurate correlations, especially within complex contexts. Therefore, we utilize Abstract Meaning Representation (AMR) graphs to assist the model in understanding complex semantic information. We introduce a method known as Graph-as-Token (GST) to incorporate AMRs into PLMs. Results from Natural Questions (NQ) and TriviaQA (TQ) demonstrate that our GST method can significantly improve performance, resulting in up to 2.44/3.17 Exact Match score improvements on NQ/TQ respectively. Furthermore, our method enhances robustness and outperforms alternative Graph Neural Network (GNN) methods for integrating AMRs. To the best of our knowledge, we are the first to employ semantic graphs in ODQA.

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RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering
Cunxiang Wang | Haofei Yu | Yue Zhang
Findings of the Association for Computational Linguistics: ACL 2023

Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of generating answers by simultaneously referring to multiple passages. Although representative models like Fusion-in-Decoder (FiD) have been proposed to address this challenge, these systems can inadvertently rely on spurious features instead of genuine causal relationships between the question and the passages to generate answers. To counter this problem, we introduce the Rational Fusion-in-Decoder (RFiD) model. Our model leverages the encoders of FiD to differentiate between causal relationships and spurious features, subsequently guiding the decoder to generate answers informed by this discernment. Experimental results on two ODQA datasets, Natural Questions (NQ) and TriviaQA (TQ), demonstrate that our model surpasses previous methods, achieving improvements of up to 1.5 and 0.7 in Exact Match scores on NQ, and exhibits an enhanced ability to identify causal relationships.

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Opinion Tree Parsing for Aspect-based Sentiment Analysis
Xiaoyi Bao | Xiaotong Jiang | Zhongqing Wang | Yue Zhang | Guodong Zhou
Findings of the Association for Computational Linguistics: ACL 2023

Extracting sentiment elements using pre-trained generative models has recently led to large improvements in aspect-based sentiment analysis benchmarks. These models avoid explicit modeling of structure between sentiment elements, which are succinct yet lack desirable properties such as structure well-formedness guarantees or built-in elements alignments. In this study, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which can explicitly reveal a more comprehensive and complete aspect-level sentiment structure. In particular, we first introduce a novel context-free opinion grammar to normalize the sentiment structure. We then employ a neural chart-based opinion tree parser to fully explore the correlations among sentiment elements and parse them in the opinion tree form. Extensive experiments show the superiority of our proposed model and the capacity of the opinion tree parser with the proposed context-free opinion grammar. More importantly, our model is much faster than previous models.

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Denoising Enhanced Distantly Supervised Ultrafine Entity Typing
Yue Zhang | Hongliang Fei | Ping Li
Findings of the Association for Computational Linguistics: ACL 2023

Recently, the task of distantly supervised (DS) ultra-fine entity typing has received significant attention. However, DS data is noisy and often suffers from missing or wrong labeling issues resulting in low precision and low recall. This paper proposes a novel ultra-fine entity typing model with denoising capability. Specifically, we build a noise model to estimate the unknown labeling noise distribution over input contexts and noisy type labels. With the noise model, more trustworthy labels can be recovered by subtracting the estimated noise from the input. Furthermore, we propose an entity typing model, which adopts a bi-encoder architecture, is trained on the denoised data. Finally, the noise model and entity typing model are trained iteratively to enhance each other. We conduct extensive experiments on the Ultra-Fine entity typing dataset as well as OntoNotes dataset and demonstrate that our approach significantly outperforms other baseline methods.

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NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts
Yue Zhang | Bo Zhang | Haochen Jiang | Zhenghua Li | Chen Li | Fei Huang | Min Zhang
Findings of the Association for Computational Linguistics: ACL 2023

We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains. Previous CGEC research primarily focuses on correcting texts from a single domain, especially learner essays. To broaden the target domain, we annotate multiple references for 12,500 sentences from three native domains, i.e., social media, scientific writing, and examination. We provide solid benchmark results for NaSGEC by employing cutting-edge CGEC models and different training data. We further perform detailed analyses of the connections and gaps between our domains from both empirical and statistical views. We hope this work can inspire future studies on an important but under-explored direction–cross-domain GEC.

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GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective
Linyi Yang | Shuibai Zhang | Libo Qin | Yafu Li | Yidong Wang | Hanmeng Liu | Jindong Wang | Xing Xie | Yue Zhang
Findings of the Association for Computational Linguistics: ACL 2023

Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase. However, the out-of-distribution (OOD) generalization problem remains a challenge in many NLP tasks, limiting the real-world deployment of these methods. This paper presents the first attempt at creating a unified benchmark named GLUE-X for evaluating OOD robustness in NLP models, highlighting the importance of OOD robustness and providing insights on how to measure the robustness of a model and how to improve it. The benchmark includes 13 publicly available datasets for OOD testing, and evaluations are conducted on 8 classic NLP tasks over 21 popularly used PLMs. Our findings confirm the need for improved OOD accuracy in NLP tasks, as significant performance degradation was observed in all settings compared to in-distribution (ID) accuracy.

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LogiCoT: Logical Chain-of-Thought Instruction Tuning
Hanmeng Liu | Zhiyang Teng | Leyang Cui | Chaoli Zhang | Qiji Zhou | Yue Zhang
Findings of the Association for Computational Linguistics: EMNLP 2023

Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enhancing the general proficiency of models. These instructions enable the model to achieve performance comparable to GPT-3.5 on general tasks like open-domain text generation and paraphrasing. However, they fall short of helping the model handle complex reasoning tasks. To bridge the gap, this paper presents LogiCoT, a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4. We elaborate on the process of harvesting instructions for prompting GPT-4 to generate chain-of-thought rationales. LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.

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TRAMS: Training-free Memory Selection for Long-range Language Modeling
Haofei Yu | Cunxiang Wang | Yue Zhang | Wei Bi
Findings of the Association for Computational Linguistics: EMNLP 2023

The Transformer architecture is crucial for numerous AI models, but it still faces challenges in long-range language modeling. Though several specific transformer architectures have been designed to tackle issues of long-range dependencies, existing methods like Transformer-XL are plagued by a high percentage of ineffective memories. In this study, we present a plug-and-play strategy, known as TRAining-free Memory Selection (TRAMS), that selects tokens participating in attention calculation based on one simple metric. This strategy allows us to keep tokens that are likely to have a high attention score with the current queries and ignore the other ones. We have tested our approach on the word-level benchmark (WikiText-103) and the character-level benchmark (enwik8), and the results indicate an improvement without having additional training or adding additional parameters.

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Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information
Yun Luo | Zhen Yang | Fandong Meng | Yingjie Li | Jie Zhou | Yue Zhang
Findings of the Association for Computational Linguistics: EMNLP 2023

Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents. Previous research has demonstrated that contextual information is crucial for developing an effective ASE model. However, we observe that merely concatenating sentences in a contextual window does not fully utilize contextual information and can sometimes lead to excessive attention on less informative sentences. To tackle this challenge, we propose an Efficient Context-aware ASE model (ECASE) that fully exploits contextual information by enhancing modeling capacity and augmenting training data. Specifically, we introduce a sequence-attention module and distance-weighted similarity loss to aggregate contextual information and argumentative information. Additionally, we augment the training data by randomly masking discourse markers and sentences, which reduces the model’s reliance on specific words or less informative sentences. Our experiments on five datasets from various domains demonstrate that our model achieves state-of-the-art performance. Furthermore, ablation studies confirm the effectiveness of each module in our model.

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Non-Autoregressive Document-Level Machine Translation
Guangsheng Bao | Zhiyang Teng | Hao Zhou | Jianhao Yan | Yue Zhang
Findings of the Association for Computational Linguistics: EMNLP 2023

Non-autoregressive translation (NAT) models achieve comparable performance and superior speed compared to auto-regressive translation (AT) models in the context of sentence-level machine translation (MT). However, their abilities are unexplored in document-level MT, hindering their usage in real scenarios. In this paper, we conduct a comprehensive examination of typical NAT models in the context of document-level MT and further propose a simple but effective design of sentence alignment between source and target. Experiments show that NAT models achieve high acceleration on documents, and sentence alignment significantly enhances their performance. However, current NAT models still have a significant performance gap compared to their AT counterparts. Further investigation reveals that NAT models suffer more from the multi-modality and misalignment issues in the context of document-level MT, and current NAT models struggle with exploiting document context and handling discourse phenomena. We delve into these challenges and provide our code at https://github.com/baoguangsheng/nat-on-doc.

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GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization
Guangsheng Bao | Zebin Ou | Yue Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques, respectively. GEMINI adaptively chooses to rewrite a specific document sentence or generate a summary sentence from scratch. Experiments demonstrate that our adaptive approach outperforms the pure abstractive and rewriting baselines on three benchmark datasets, achieving the best results on WikiHow. Interestingly, empirical results show that the human summary styles of summary sentences are consistently predictable given their context. We release our code and model at https://github.com/baoguangsheng/gemini.

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Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Tianhang Zhang | Lin Qiu | Qipeng Guo | Cheng Deng | Yue Zhang | Zheng Zhang | Chenghu Zhou | Xinbing Wang | Luoyi Fu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsensical outputs that fail to meet user expectations in many real-world applications. Existing works for detecting hallucinations in LLMs either rely on external knowledge for reference retrieval or require sampling multiple responses from the LLM for consistency verification, making these methods costly and inefficient. In this paper, we propose a novel reference-free, uncertainty-based method for detecting hallucinations in LLMs. Our approach imitates human focus in factuality checking from three aspects: 1) focus on the most informative and important keywords in the given text; 2) focus on the unreliable tokens in historical context which may lead to a cascade of hallucinations; and 3) focus on the token properties such as token type and token frequency. Experimental results on relevant datasets demonstrate the effectiveness of our proposed method, which achieves state-of-the-art performance across all the evaluation metrics and eliminates the need for additional information.

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Memory-Based Invariance Learning for Out-of-Domain Text Classification
Chen Jia | Yue Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

We investigate the task of out-of-domain (OOD) text classification with the aim of extending a classification model, trained on multiple source domains, to an unseen target domain. Recent studies have shown that learning invariant representations can enhance the performance of OOD generalization. However, the inherent disparity in data distribution across different domains poses challenges for achieving effective invariance learning. This study addresses this issue by employing memory augmentations. Specifically, we augment the original feature space using key-value memory and employ a meta-learning-based approach to enhance the quality of the invariant representations. Experimental results on sentiment analysis and natural language inference tasks show the effectiveness of memory-based method for invariance learning, leading to state-of-the-art performance on six datasets.

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RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
Fengji Zhang | Bei Chen | Yue Zhang | Jacky Keung | Jin Liu | Daoguang Zan | Yi Mao | Jian-Guang Lou | Weizhu Chen
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

The task of repository-level code completion is to continue writing the unfinished code based on a broader context of the repository. While for automated code completion tools, it is difficult to utilize the useful information scattered in different files. We propose RepoCoder, a simple, generic, and effective framework to address the challenge. It streamlines the repository-level code completion process by incorporating a similarity-based retriever and a pre-trained code language model in an iterative retrieval-generation pipeline. RepoCoder makes effective utilization of repository-level information for code completion and has the ability to generate code at various levels of granularity. Moreover, we propose a new benchmark RepoBench, which consists of the latest and high-quality real-world repositories covering line, API invocation, and function body completion scenarios. Experimental results indicate that RepoCoder significantly improves the In-File completion baseline by over 10% in all settings and consistently outperforms the vanilla retrieval-augmented code completion approach. Furthermore, we validate the effectiveness of RepoCoder through comprehensive analysis, providing valuable insights for future research. Our source code and benchmark will be publicly available after the paper review.

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Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts
Tengxiao Liu | Qipeng Guo | Yuqing Yang | Xiangkun Hu | Yue Zhang | Xipeng Qiu | Zheng Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

As large language models (LLMs) have shown effectiveness with different prompting methods, such as Chain of Thought, Program of Thought, we find that these methods have formed a great complementarity to each other on math reasoning tasks. In this work, we propose XoT, an integrated problem solving framework by prompting LLMs with diverse reasoning thoughts. For each question, XoT always begins with selecting the most suitable method then executes each method iteratively. Within each iteration, XoT actively checks the validity of the generated answer and incorporates the feedback from external executors, allowing it to dynamically switch among different prompting methods. Through extensive experiments on 10 popular math reasoning datasets, we demonstrate the effectiveness of our proposed approach and thoroughly analyze the strengths of each module. Moreover, empirical results suggest that our framework is orthogonal to recent work that makes improvements on single reasoning methods and can further generalise to logical reasoning domain. By allowing method switching, XoT provides a fresh perspective on the collaborative integration of diverse reasoning thoughts in a unified framework.

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Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future
Linyi Yang | Yaoxian Song | Xuan Ren | Chenyang Lyu | Yidong Wang | Jingming Zhuo | Lingqiao Liu | Jindong Wang | Jennifer Foster | Yue Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differs from the training data distribution. This poses important questions about the robustness of NLP models and their high accuracy, which may be artificially inflated due to their underlying sensitivity to systematic biases. Despite these challenges, there is a lack of comprehensive surveys on the generalization challenge from an OOD perspective in natural language understanding. Therefore, this paper aims to fill this gap by presenting the first comprehensive review of recent progress, methods, and evaluations on this topic. We further discuss the challenges involved and potential future research directions. By providing convenient access to existing work, we hope this survey will encourage future research in this area.

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End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions
Libo Qin | Wenbo Pan | Qiguang Chen | Lizi Liao | Zhou Yu | Yue Zhang | Wanxiang Che | Min Li
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this research field; (2) New taxonomy: we first introduce a unified perspective for EToD, including (i) Modularly EToD and (ii) Fully EToD; (3) New Frontiers: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) Abundant resources: we build a public website, where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community.

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Non-autoregressive Text Editing with Copy-aware Latent Alignments
Yu Zhang | Yue Zhang | Leyang Cui | Guohong Fu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Recent work has witnessed a paradigm shift from Seq2Seq to Seq2Edit in the field of text editing, with the aim of addressing the slow autoregressive inference problem posed by the former. Despite promising results, Seq2Edit approaches still face several challenges such as inflexibility in generation and difficulty in generalizing to other languages. In this work, we propose a novel non-autoregressive text editing method to circumvent the above issues, by modeling the edit process with latent CTC alignments. We make a crucial extension to CTC by introducing the copy operation into the edit space, thus enabling more efficient management of textual overlap in editing. We conduct extensive experiments on GEC and sentence fusion tasks, showing that our proposed method significantly outperforms existing Seq2Edit models and achieves similar or even better results than Seq2Seq with over speedup. Moreover, it demonstrates good generalizability on German and Russian. In-depth analyses reveal the strengths of our method in terms of the robustness under various scenarios and generating fluent and flexible outputs.

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LLM-enhanced Self-training for Cross-domain Constituency Parsing
Jianling Li | Meishan Zhang | Peiming Guo | Min Zhang | Yue Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Self-training has proven to be an effective approach for cross-domain tasks, and in this study, we explore its application to cross-domain constituency parsing. Traditional self-training methods rely on limited and potentially low-quality raw corpora. To overcome this limitation, we propose enhancing self-training with the large language model (LLM) to generate domain-specific raw corpora iteratively. For the constituency parsing, we introduce grammar rules that guide the LLM in generating raw corpora and establish criteria for selecting pseudo instances. Our experimental results demonstrate that self-training for constituency parsing, equipped with an LLM, outperforms traditional methods regardless of the LLM’s performance. Moreover, the combination of grammar rules and confidence criteria for pseudo-data selection yields the highest performance in the cross-domain constituency parsing.

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StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding
Cheng Jiayang | Lin Qiu | Tsz Chan | Tianqing Fang | Weiqi Wang | Chunkit Chan | Dongyu Ru | Qipeng Guo | Hongming Zhang | Yangqiu Song | Yue Zhang | Zheng Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Analogy-making between narratives is crucial for human reasoning. In this paper, we evaluate the ability to identify and generate analogies by constructing a first-of-its-kind large-scale story-level analogy corpus, StoryAnalogy, which contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory. We design a set of tests on StoryAnalogy, presenting the first evaluation of story-level analogy identification and generation. Interestingly, we find that the analogy identification tasks are incredibly difficult not only for sentence embedding models but also for the recent large language models (LLMs) such as ChatGPT and LLaMa. ChatGPT, for example, only achieved around 30% accuracy in multiple-choice questions (compared to over 85% accuracy for humans). Furthermore, we observe that the data in StoryAnalogy can improve the quality of analogy generation in LLMs, where a fine-tuned FlanT5-xxl model achieves comparable performance to zero-shot ChatGPT.

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RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation
Yue Zhang | Leyang Cui | Enbo Zhao | Wei Bi | Shuming Shi
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Grammatical Error Correction (GEC) systems play a vital role in assisting people with their daily writing tasks. However, users may sometimes come across a GEC system that initially performs well but fails to correct errors when the inputs are slightly modified. To ensure an ideal user experience, a reliable GEC system should have the ability to provide consistent and accurate suggestions when encountering irrelevant context perturbations, which we refer to as context robustness. In this paper, we introduce RobustGEC, a benchmark designed to evaluate the context robustness of GEC systems. RobustGEC comprises 5,000 GEC cases, each with one original error-correct sentence pair and five variants carefully devised by human annotators. Utilizing RobustGEC, we reveal that state-of-the-art GEC systems still lack sufficient robustness against context perturbations. Moreover, we propose a simple yet effective method for remitting this issue.

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Token-level Fitting Issues of Seq2seq Models
Guangsheng Bao | Zhiyang Teng | Yue Zhang
Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)

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Eliciting Rich Positive Emotions in Dialogue Generation
Ziwei Gong | Qingkai Min | Yue Zhang
Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)

Positive emotion elicitation aims at evoking positive emotion states in human users in open-domain dialogue generation. However, most work focuses on inducing a single-dimension of positive sentiment using human annotated datasets, which limits the scale of the training dataset. In this paper, we propose to model various emotions in large unannotated conversations, such as joy, trust and anticipation, by leveraging a latent variable to control the emotional intention of the response. Our proposed emotion-eliciting-Conditional-Variational-AutoEncoder (EE-CVAE) model generates more diverse and emotionally-intelligent responses compared to single-dimension baseline models in human evaluation.

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基于网络词典的现代汉语词义消歧数据集构建(Construction of a Modern Chinese Word Sense Dataset Based on Online Dictionaries)
Fukang Yan (严福康) | Yue Zhang (章岳) | Zhenghua Li (李正华)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics

“词义消歧作为自然语言处理最经典的任务之一,旨在识别多义词在给定上下文中的正确词义。相比英文,中文的一词多义现象更普遍,然而当前公开发布的汉语词义消歧数据集很少。本文爬取并融合了两个公开的网络词典,并从中筛选1083个词语和相关义项作为待标注对象。进而,从网络数据及专业语料中为抽取相关句子。最后,以多人标注、专家审核的方式进行了人工标注。数据集1包含将近2万个句子,即每个词平均对应约20个句子。本文将数据集划分为训练集、验证集和测试集,对多种模型进行实验对比。”

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CCL23-Eval 任务2系统报告:WestlakeNLP,基于生成式大语言模型的中文抽象语义表示解析(System Report for CCL23-Eval Task 2: WestlakeNLP, Investigating Generative Large Language Models for Chinese AMR Parsing)
Wenyang Gao (高文炀) | Xuefeng Bai (白雪峰) | Yue Zhang (张岳)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)

“本文介绍了我们在第二十二届中文计算语言学大会中文抽象语义表示解析评测任务中提交的参赛系统。中文抽象语义表示(Chinese Abstract Meaning Representa-tion,CAMR)不仅以图的方式表示句子的语义,还保证了概念对齐和关系对齐。近期,生成式大规模语言模型在诸多自然语言处理任务上展现了优秀的生成能力和泛化能力。受此启发,我们选择微调Baichuan-7B模型来以端到端的形式从文本直接生成序列化的CAMR。实验结果表明,我们的系统能够在不依赖于词性、依存句法信息以及复杂规则的前提下取得了同现有方法可比的性能。”

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OpenFact: Factuality Enhanced Open Knowledge Extraction
Linfeng Song | Ante Wang | Xiaoman Pan | Hongming Zhang | Dian Yu | Lifeng Jin | Haitao Mi | Jinsong Su | Yue Zhang | Dong Yu
Transactions of the Association for Computational Linguistics, Volume 11

We focus on the factuality property during the extraction of an OpenIE corpus named OpenFact, which contains more than 12 million high-quality knowledge triplets. We break down the factuality property into two important aspects—expressiveness and groundedness—and we propose a comprehensive framework to handle both aspects. To enhance expressiveness, we formulate each knowledge piece in OpenFact based on a semantic frame. We also design templates, extra constraints, and adopt human efforts so that most OpenFact triplets contain enough details. For groundedness, we require the main arguments of each triplet to contain linked Wikidata1 entities. A human evaluation suggests that the OpenFact triplets are much more accurate and contain denser information compared to OPIEC-Linked (Gashteovski et al., 2019), one recent high-quality OpenIE corpus grounded to Wikidata. Further experiments on knowledge base completion and knowledge base question answering show the effectiveness of OpenFact over OPIEC-Linked as supplementary knowledge to Wikidata as the major KG.

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Consistency Regularization Training for Compositional Generalization
Yongjing Yin | Jiali Zeng | Yafu Li | Fandong Meng | Jie Zhou | Yue Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Existing neural models have difficulty generalizing to unseen combinations of seen components. To achieve compositional generalization, models are required to consistently interpret (sub)expressions across contexts. Without modifying model architectures, we improve the capability of Transformer on compositional generalization through consistency regularization training, which promotes representation consistency across samples and prediction consistency for a single sample. Experimental results on semantic parsing and machine translation benchmarks empirically demonstrate the effectiveness and generality of our method. In addition, we find that the prediction consistency scores on in-distribution validation sets can be an alternative for evaluating models during training, when commonly-used metrics are not informative.

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Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations
Dongyu Ru | Lin Qiu | Xipeng Qiu | Yue Zhang | Zheng Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Discourse analysis is an important task because it models intrinsic semantic structures between sentences in a document. Discourse markers are natural representations of discourse in our daily language. One challenge is that the markers as well as pre-defined and human-labeled discourse relations can be ambiguous when describing the semantics between sentences. We believe that a better approach is to use a contextual-dependent distribution over the markers to express discourse information. In this work, we propose to learn a Distributed Marker Representation (DMR) by utilizing the (potentially) unlimited discourse marker data with a latent discourse sense, thereby bridging markers with sentence pairs. Such representations can be learned automatically from data without supervision, and in turn provide insights into the data itself. Experiments show the SOTA performance of our DMR on the implicit discourse relation recognition task and strong interpretability. Our method also offers a valuable tool to understand complex ambiguity and entanglement among discourse markers and manually defined discourse relations.

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Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation
Yulong Chen | Huajian Zhang | Yijie Zhou | Xuefeng Bai | Yueguan Wang | Ming Zhong | Jianhao Yan | Yafu Li | Judy Li | Xianchao Zhu | Yue Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Most existing cross-lingual summarization (CLS) work constructs CLS corpora by simply and directly translating pre-annotated summaries from one language to another, which can contain errors from both summarization and translation processes. To address this issue, we propose ConvSumX, a cross-lingual conversation summarization benchmark, through a new annotation schema that explicitly considers source input context. ConvSumX consists of 2 sub-tasks under different real-world scenarios, with each covering 3 language directions. We conduct thorough analysis on ConvSumX and 3 widely-used manually annotated CLS corpora and empirically find that ConvSumX is more faithful towards input text. Additionally, based on the same intuition, we propose a 2-Step method, which takes both conversation and summary as input to simulate human annotation process. Experimental results show that 2-Step method surpasses strong baselines on ConvSumX under both automatic and human evaluation. Analysis shows that both source input text and summary are crucial for modeling cross-lingual summaries.

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Target-Side Augmentation for Document-Level Machine Translation
Guangsheng Bao | Zhiyang Teng | Yue Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious patterns. To address this challenge, we propose a target-side augmentation method, introducing a data augmentation (DA) model to generate many potential translations for each source document. Learning on these wider range translations, an MT model can learn a smoothed distribution, thereby reducing the risk of data sparsity. We demonstrate that the DA model, which estimates the posterior distribution, largely improves the MT performance, outperforming the previous best system by 2.30 s-BLEU on News and achieving new state-of-the-art on News and Europarl benchmarks.

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UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization
Yulong Chen | Yang Liu | Ruochen Xu | Ziyi Yang | Chenguang Zhu | Michael Zeng | Yue Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The high annotation costs and diverse demands of various summarization tasks motivate the development of few-shot summarization. However, despite the emergence of many summarization tasks and datasets, the current training paradigm for few-shot summarization systems ignores potentially shareable knowledge in heterogeneous datasets. To this end, we propose UniSumm, a unified few-shot summarization model pre-trained with multiple summarization tasks and can be prefix-tuned to excel at any few-shot summarization task. Meanwhile, to better evaluate few-shot summarizers, under the principles of diversity and robustness, we assemble and release a new benchmark SummZoo. It consists of 8 summarization tasks with multiple sets of few-shot samples for each task, covering diverse domains. Experimental results and analysis show that UniSumm outperforms strong baselines by a large margin across all sub-tasks in SummZoo under both automatic and human evaluations and achieves comparable results in human evaluation compared with a GPT-3.5 model.

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An AMR-based Link Prediction Approach for Document-level Event Argument Extraction
Yuqing Yang | Qipeng Guo | Xiangkun Hu | Yue Zhang | Xipeng Qiu | Zheng Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a useful interpretation of complex semantic structures and helps to capture long-distance dependency. However, in these works AMR is used only implicitly, for instance, as additional features or training signals. Motivated by the fact that all event structures can be inferred from AMR, this work reformulates EAE as a link prediction problem on AMR graphs. Since AMR is a generic structure and does not perfectly suit EAE, we propose a novel graph structure, Tailored AMR Graph (TAG), which compresses less informative subgraphs and edge types, integrates span information, and highlights surrounding events in the same document. With TAG, we further propose a novel method using graph neural networks as a link prediction model to find event arguments. Our extensive experiments on WikiEvents and RAMS show that this simpler approach outperforms the state-of-the-art models by 3.63pt and 2.33pt F1, respectively, and do so with reduced 56% inference time.

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VLN-Trans: Translator for the Vision and Language Navigation Agent
Yue Zhang | Parisa Kordjamshidi
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Language understanding is essential for the navigation agent to follow instructions. We observe two kinds of issues in the instructions that can make the navigation task challenging: 1. The mentioned landmarks are not recognizable by the navigation agent due to the different vision abilities of the instructor and the modeled agent. 2. The mentioned landmarks are applicable to multiple targets, thus not distinctive for selecting the target among the candidate viewpoints. To deal with these issues, we design a translator module for the navigation agent to convert the original instructions into easy-to-follow sub-instruction representations at each step. The translator needs to focus on the recognizable and distinctive landmarks based on the agent’s visual abilities and the observed visual environment. To achieve this goal, we create a new synthetic sub-instruction dataset and design specific tasks to train the translator and the navigation agent. We evaluate our approach on Room2Room (R2R), Room4room (R4R), and Room2Room Last (R2R-Last) datasets and achieve state-of-the-art results on multiple benchmarks.

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Measuring Consistency in Text-based Financial Forecasting Models
Linyi Yang | Yingpeng Ma | Yue Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Financial forecasting has been an important and active area of machine learning research, as even the most modest advantages in predictive accuracy can be parlayed into significant financial gains. Recent advances in natural language processing (NLP) bring the opportunity to leverage textual data, such as earnings reports of publicly traded companies, to predict the return rate for an asset. However, when dealing with such a sensitive task, the consistency of models – their invariance under meaning-preserving alternations in input – is a crucial property for building user trust. Despite this, current methods for financial forecasting do not take consistency into consideration. To address this issue, we propose FinTrust, an evaluation tool that assesses logical consistency in financial text. Using FinTrust, we show that the consistency of state-of-the-art NLP models for financial forecasting is poor. Our analysis of the performance degradation caused by meaning-preserving alternations suggests that current text-based methods are not suitable for robustly predicting market information.

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Explicit Syntactic Guidance for Neural Text Generation
Yafu Li | Leyang Cui | Jianhao Yan | Yongjing Yin | Wei Bi | Shuming Shi | Yue Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Most existing text generation models follow the sequence-to-sequence paradigm. Generative Grammar suggests that humans generate natural language texts by learning language grammar. We propose a syntax-guided generation schema, which generates the sequence guided by a constituency parse tree in a top-down direction. The decoding process can be decomposed into two parts: (1) predicting the infilling texts for each constituent in the lexicalized syntax context given the source sentence; (2) mapping and expanding each constituent to construct the next-level syntax context. Accordingly, we propose a structural beam search method to find possible syntax structures hierarchically. Experiments on paraphrase generation and machine translation show that the proposed method outperforms autoregressive baselines, while also demonstrating effectiveness in terms of interpretability, controllability, and diversity.

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Dual Cache for Long Document Neural Coreference Resolution
Qipeng Guo | Xiangkun Hu | Yue Zhang | Xipeng Qiu | Zheng Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent works show the effectiveness of cache-based neural coreference resolution models on long documents. These models incrementally process a long document from left to right and extract relations between mentions and entities in a cache, resulting in much lower memory and computation cost compared to computing all mentions in parallel. However, they do not handle cache misses when high-quality entities are purged from the cache, which causes wrong assignments and leads to prediction errors. We propose a new hybrid cache that integrates two eviction policies to capture global and local entities separately, and effectively reduces the aggregated cache misses up to half as before, while improving F1 score of coreference by 0.7 5.7pt. As such, the hybrid policy can accelerate existing cache-based models and offer a new long document coreference resolution solution. Results show that our method outperforms existing methods on four benchmarks while saving up to 83% of inference time against non-cache-based models. Further, we achieve a new state-of-the-art on a long document coreference benchmark, LitBank.

2022

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Challenges to Open-Domain Constituency Parsing
Sen Yang | Leyang Cui | Ruoxi Ning | Di Wu | Yue Zhang
Findings of the Association for Computational Linguistics: ACL 2022

Neural constituency parsers have reached practical performance on news-domain benchmarks. However, their generalization ability to other domains remains weak. Existing findings on cross-domain constituency parsing are only made on a limited number of domains. Tracking this, we manually annotate a high-quality constituency treebank containing five domains. We analyze challenges to open-domain constituency parsing using a set of linguistic features on various strong constituency parsers. Primarily, we find that 1) BERT significantly increases parsers’ cross-domain performance by reducing their sensitivity on the domain-variant features.2) Compared with single metrics such as unigram distribution and OOV rate, challenges to open-domain constituency parsing arise from complex features, including cross-domain lexical and constituent structure variations.

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Prompt-Driven Neural Machine Translation
Yafu Li | Yongjing Yin | Jing Li | Yue Zhang
Findings of the Association for Computational Linguistics: ACL 2022

Neural machine translation (NMT) has obtained significant performance improvement over the recent years. However, NMT models still face various challenges including fragility and lack of style flexibility. Moreover, current methods for instance-level constraints are limited in that they are either constraint-specific or model-specific. To this end, we propose prompt-driven neural machine translation to incorporate prompts for enhancing translation control and enriching flexibility. Empirical results demonstrate the effectiveness of our method in both prompt responding and translation quality. Through human evaluation, we further show the flexibility of prompt control and the efficiency in human-in-the-loop translation.

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A Graph Enhanced BERT Model for Event Prediction
Li Du | Xiao Ding | Yue Zhang | Ting Liu | Bing Qin
Findings of the Association for Computational Linguistics: ACL 2022

Predicting the subsequent event for an existing event context is an important but challenging task, as it requires understanding the underlying relationship between events. Previous methods propose to retrieve relational features from event graph to enhance the modeling of event correlation. However, the sparsity of event graph may restrict the acquisition of relevant graph information, and hence influence the model performance. To address this issue, we consider automatically building of event graph using a BERT model. To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process. Hence, in the test process, the connection relationship for unseen events can be predicted by the structured variable. Results on two event prediction tasks: script event prediction and story ending prediction, show that our approach can outperform state-of-the-art baseline methods.

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PromptGen: Automatically Generate Prompts using Generative Models
Yue Zhang | Hongliang Fei | Dingcheng Li | Ping Li
Findings of the Association for Computational Linguistics: NAACL 2022

Recently, prompt learning has received significant attention, where the downstream tasks are reformulated to the mask-filling task with the help of a textual prompt. The key point of prompt learning is finding the most appropriate prompt. This paper proposes a novel model PromptGen, which can automatically generate prompts conditional on the input sentence. PromptGen is the first work considering dynamic prompt generation for knowledge probing, based on a pre-trained generative model. To mitigate any label information leaking from the pre-trained generative model, when given a generated prompt, we replace the query input with “None”. We pursue that this perturbed context-free prompt cannot trigger the correct label. We evaluate our model on the knowledge probing LAMA benchmark, and show that PromptGen significantly outperforms other baselines.

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Findings of the Association for Computational Linguistics: EMNLP 2022
Yoav Goldberg | Zornitsa Kozareva | Yue Zhang
Findings of the Association for Computational Linguistics: EMNLP 2022

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AdaPrompt: Adaptive Model Training for Prompt-based NLP
Yulong Chen | Yang Liu | Li Dong | Shuohang Wang | Chenguang Zhu | Michael Zeng | Yue Zhang
Findings of the Association for Computational Linguistics: EMNLP 2022

Prompt-based learning, with its capability to tackle zero-shot and few-shot NLP tasks, has gained much attention in the community. The main idea is to bridge the gap between NLP downstream tasks and language modeling (LM), by mapping these tasks into natural language prompts, which are then filled by pre-trained language models (PLMs).However, for prompt learning, there are still two salient gaps between NLP tasks and pretraining. First, prompt information is not necessarily sufficiently present during LM pre-training. Second, task-specific data are not necessarily well represented during pre-training. We address these two issues by proposing AdaPrompt, adaptively retrieving external data for continual pretraining of PLMs by making use of both task and prompt characteristics. In addition, we make use of knowledge in Natural Language Inference models for deriving adaptive verbalizers.Experimental results on five NLP benchmarks show that AdaPrompt can improve over standard PLMs in few-shot settings. In addition, in zero-shot settings, our method outperforms standard prompt-based methods by up to 26.35% relative error reduction.

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The Cross-lingual Conversation Summarization Challenge
Yulong Chen | Ming Zhong | Xuefeng Bai | Naihao Deng | Jing Li | Xianchao Zhu | Yue Zhang
Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges

We propose the shared task of cross-lingual conversation summarization, ConvSumX Challenge, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine translation. This task can be particularly useful due to the emergence of online meetings and conferences. We use a new benchmark, covering 2 real-world scenarios and 3 language directions, including a low-resource language, for evaluation. We hope that ConvSumX can motivate research to go beyond English and break the barrier for non-English speakers to benefit from recent advances of conversation summarization.

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DialogSum Challenge: Results of the Dialogue Summarization Shared Task
Yulong Chen | Naihao Deng | Yang Liu | Yue Zhang
Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges

We report the results of DialogSum Challenge, the shared task on summarizing real-life sce- nario dialogues at INLG 2022. Four teams participate in this shared task and three submit their system reports, exploring different meth- ods to improve the performance of dialogue summarization. Although there is a great im- provement over the baseline models regarding automatic evaluation metrics, such as ROUGE scores, we find that there is a salient gap be- tween model generated outputs and human an- notated summaries by human evaluation from multiple aspects. These findings demonstrate the difficulty of dialogue summarization and suggest that more fine-grained evaluatuion met- rics are in need.

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Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)
Lingfei Wu | Bang Liu | Rada Mihalcea | Jian Pei | Yue Zhang | Yunyao Li
Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)

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Discrete Opinion Tree Induction for Aspect-based Sentiment Analysis
Chenhua Chen | Zhiyang Teng | Zhongqing Wang | Yue Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Dependency trees have been intensively used with graph neural networks for aspect-based sentiment classification. Though being effective, such methods rely on external dependency parsers, which can be unavailable for low-resource languages or perform worse in low-resource domains. In addition, dependency trees are also not optimized for aspect-based sentiment classification. In this paper, we propose an aspect-specific and language-agnostic discrete latent opinion tree model as an alternative structure to explicit dependency trees. To ease the learning of complicated structured latent variables, we build a connection between aspect-to-context attention scores and syntactic distances, inducing trees from the attention scores. Results on six English benchmarks and one Chinese dataset show that our model can achieve competitive performance and interpretability.

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Investigating Non-local Features for Neural Constituency Parsing
Leyang Cui | Sen Yang | Yue Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Thanks to the strong representation power of neural encoders, neural chart-based parsers have achieved highly competitive performance by using local features. Recently, it has been shown that non-local features in CRF structures lead to improvements. In this paper, we investigate injecting non-local features into the training process of a local span-based parser, by predicting constituent n-gram non-local patterns and ensuring consistency between non-local patterns and local constituents. Results show that our simple method gives better results than the self-attentive parser on both PTB and CTB. Besides, our method achieves state-of-the-art BERT-based performance on PTB (95.92 F1) and strong performance on CTB (92.31 F1). Our parser also outperforms the self-attentive parser in multi-lingual and zero-shot cross-domain settings.

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Graph Pre-training for AMR Parsing and Generation
Xuefeng Bai | Yulong Chen | Yue Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. Recently, pre-trained language models (PLMs) have advanced tasks of AMR parsing and AMR-to-text generation, respectively. However, PLMs are typically pre-trained on textual data, thus are sub-optimal for modeling structural knowledge. To this end, we investigate graph self-supervised training to improve the structure awareness of PLMs over AMR graphs. In particular, we introduce two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-training. We further design a unified framework to bridge the gap between pre-training and fine-tuning tasks. Experiments on both AMR parsing and AMR-to-text generation show the superiority of our model. To our knowledge, we are the first to consider pre-training on semantic graphs.

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A Rationale-Centric Framework for Human-in-the-loop Machine Learning
Jinghui Lu | Linyi Yang | Brian Namee | Yue Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a novel rational-centric framework with human-in-the-loop – Rationales-centric Double-robustness Learning (RDL) – to boost model out-of-distribution performance in few-shot learning scenarios. By using static semi-factual generation and dynamic human-intervened correction, RDL, acting like a sensible “inductive bias”, exploits rationales (i.e. phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation. Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests, especially for few-shot learning scenarios, compared to many state-of-the-art benchmarks. We also perform extensive ablation studies to support in-depth analyses of each component in our framework.

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Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Jiangbin Zheng | Yile Wang | Ge Wang | Jun Xia | Yufei Huang | Guojiang Zhao | Yue Zhang | Stan Li
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e.g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability. In this paper, we aim to improve word embeddings by 1) incorporating more contextual information from existing pre-trained models into the Skip-gram framework, which we call Context-to-Vec; 2) proposing a post-processing retrofitting method for static embeddings independent of training by employing priori synonym knowledge and weighted vector distribution. Through extrinsic and intrinsic tasks, our methods are well proven to outperform the baselines by a large margin.

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Explicit Object Relation Alignment for Vision and Language Navigation
Yue Zhang | Parisa Kordjamshidi
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

In this paper, we investigate the problem of vision and language navigation. To solve this problem, grounding the landmarks and spatial relations in the textual instructions into visual modality is important. We propose a neural agent named Explicit Object Relation Alignment Agent (EXOR),to explicitly align the spatial information in both instruction and the visual environment, including landmarks and spatial relationships between the agent and landmarks. Empirically, our proposed method surpasses the baseline by a large margin on the R2R dataset. We provide a comprehensive analysis to show our model’s spatial reasoning ability and explainability.

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Semantic-based Pre-training for Dialogue Understanding
Xuefeng Bai | Linfeng Song | Yue Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Pre-trained language models have made great progress on dialogue tasks. However, these models are typically trained on surface dialogue text, thus are proven to be weak in understanding the main semantic meaning of a dialogue context. We investigate Abstract Meaning Representation (AMR) as explicit semantic knowledge for pre-training models to capture the core semantic information in dialogues during pre-training. In particular, we propose a semantic-based pre-training framework that extends the standard pre-training framework (Devlin et al.,2019) by three tasks for learning 1) core semantic units, 2) semantic relations and 3) the overall semantic representation according to AMR graphs. Experiments on the understanding of both chit-chats and task-oriented dialogues show the superiority of our model. To our knowledge, we are the first to leverage a deep semantic representation for dialogue pre-training.

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Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect
Naihao Deng | Yulong Chen | Yue Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Text-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL queries and its practical application in building natural language interfaces to database systems. The major challenges in text-to-SQL lie in encoding the meaning of natural utterances, decoding to SQL queries, and translating the semantics between these two forms. These challenges have been addressed to different extents by the recent advances. However, there is still a lack of comprehensive surveys for this task. To this end, we review recent progress on text-to-SQL for datasets, methods, and evaluation and provide this systematic survey, addressing the aforementioned challenges and discussing potential future directions. We hope this survey can serve as quick access to existing work and motivate future research.

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Human-in-the-loop Robotic Grasping Using BERT Scene Representation
Yaoxian Song | Penglei Sun | Pengfei Fang | Linyi Yang | Yanghua Xiao | Yue Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Current NLP techniques have been greatly applied in different domains. In this paper, we propose a human-in-the-loop framework for robotic grasping in cluttered scenes, investigating a language interface to the grasping process, which allows the user to intervene by natural language commands. This framework is constructed on a state-of-the-art grasping baseline, where we substitute a scene-graph representation with a text representation of the scene using BERT. Experiments on both simulation and physical robot show that the proposed method outperforms conventional object-agnostic and scene-graph based methods in the literature. In addition, we find that with human intervention, performance can be significantly improved. Our dataset and code are available on our project website https://sites.google.com/view/hitl-grasping-bert.

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Speeding up Transformer Decoding via an Attention Refinement Network
Kaixin Wu | Yue Zhang | Bojie Hu | Tong Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Despite the revolutionary advances made by Transformer in Neural Machine Translation (NMT), inference efficiency remains an obstacle due to the heavy use of attention operations in auto-regressive decoding. We thereby propose a lightweight attention structure called Attention Refinement Network (ARN) for speeding up Transformer. Specifically, we design a weighted residual network, which reconstructs the attention by reusing the features across layers. To further improve the Transformer efficiency, we merge the self-attention and cross-attention components for parallel computing. Extensive experiments on ten WMT machine translation tasks show that the proposed model yields an average of 1.35x faster (with almost no decrease in BLEU) over the state-of-the-art inference implementation. Results on widely used WMT14 En-De machine translation tasks demonstrate that our model achieves a higher speed-up, giving highly competitive performance compared to AAN and SAN models with fewer parameter numbers.

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Categorizing Semantic Representations for Neural Machine Translation
Yongjing Yin | Yafu Li | Fandong Meng | Jie Zhou | Yue Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks. However, they have recently been shown to suffer limitation in compositional generalization, failing to effectively learn the translation of atoms (e.g., words) and their semantic composition (e.g., modification) from seen compounds (e.g., phrases), and thus suffering from significantly weakened translation performance on unseen compounds during inference. We address this issue by introducing categorization to the source contextualized representations. The main idea is to enhance generalization by reducing sparsity and overfitting, which is achieved by finding prototypes of token representations over the training set and integrating their embeddings into the source encoding. Experiments on a dedicated MT dataset (i.e., CoGnition) show that our method reduces compositional generalization error rates by 24% error reduction. In addition, our conceptually simple method gives consistently better results than the Transformer baseline on a range of general MT datasets.

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FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition
Linyi Yang | Lifan Yuan | Leyang Cui | Wenyang Gao | Yue Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years. Existing approaches for few-shot NER are evaluated mainly under in-domain settings. In contrast, little is known about how these inherently faithful models perform in cross-domain NER using a few labeled in-domain examples. This paper proposes a two-step rationale-centric data augmentation method to improve the model’s generalization ability. Results on several datasets show that our model-agnostic method significantly improves the performance of cross-domain NER tasks compared to previous state-of-the-art methods compared to the counterfactual data augmentation and prompt-tuning methods.

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LOViS: Learning Orientation and Visual Signals for Vision and Language Navigation
Yue Zhang | Parisa Kordjamshidi
Proceedings of the 29th International Conference on Computational Linguistics

Understanding spatial and visual information is essential for a navigation agent who follows natural language instructions. The current Transformer-based VLN agents entangle the orientation and vision information, which limits the gain from the learning of each information source. In this paper, we design a neural agent with explicit Orientation and Vision modules. Those modules learn to ground spatial information and landmark mentions in the instructions to the visual environment more effectively. To strengthen the spatial reasoning and visual perception of the agent, we design specific pre-training tasks to feed and better utilize the corresponding modules in our final navigation model. We evaluate our approach on both Room2room (R2R) and Room4room (R4R) datasets and achieve the state of the art results on both benchmarks.

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On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART
Zebin Ou | Meishan Zhang | Yue Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Word ordering is a constrained language generation task taking unordered words as input. Existing work uses linear models and neural networks for the task, yet pre-trained language models have not been studied in word ordering, let alone why they help. We use BART as an instance and show its effectiveness in the task. To explain why BART helps word ordering, we extend analysis with probing and empirically identify that syntactic dependency knowledge in BART is a reliable explanation. We also report performance gains with BART in the related partial tree linearization task, which readily extends our analysis.

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Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction
Yidong Wang | Hao Wu | Ao Liu | Wenxin Hou | Zhen Wu | Jindong Wang | Takahiro Shinozaki | Manabu Okumura | Yue Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment analysis task that aims to extract the corresponding opinion words of a given opinion target from the sentence. Recently, deep learning approaches have made remarkable progress on this task. Nevertheless, the TOWE task still suffers from the scarcity of training data due to the expensive data annotation process. Limited labeled data increase the risk of distribution shift between test data and training data. In this paper, we propose exploiting massive unlabeled data to reduce the risk by increasing the exposure of the model to varying distribution shifts. Specifically, we propose a novel Multi-Grained Consistency Regularization (MGCR) method to make use of unlabeled data and design two filters specifically for TOWE to filter noisy data at different granularity. Extensive experimental results on four TOWE benchmark datasets indicate the superiority of MGCR compared with current state-of-the-art methods. The in-depth analysis also demonstrates the effectiveness of the different-granularity filters.

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Mere Contrastive Learning for Cross-Domain Sentiment Analysis
Yun Luo | Fang Guo | Zihan Liu | Yue Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Cross-domain sentiment analysis aims to predict the sentiment of texts in the target domain using the model trained on the source domain to cope with the scarcity of labeled data. Previous studies are mostly cross-entropy-based methods for the task, which suffer from instability and poor generalization. In this paper, we explore contrastive learning on the cross-domain sentiment analysis task. We propose a modified contrastive objective with in-batch negative samples so that the sentence representations from the same class can be pushed close while those from the different classes become further apart in the latent space. Experiments on two widely used datasets show that our model can achieve state-of-the-art performance in both cross-domain and multi-domain sentiment analysis tasks. Meanwhile, visualizations demonstrate the effectiveness of transferring knowledge learned in the source domain to the target domain and the adversarial test verifies the robustness of our model.

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Exploiting Sentiment and Common Sense for Zero-shot Stance Detection
Yun Luo | Zihan Liu | Yuefeng Shi | Stan Z. Li | Yue Zhang
Proceedings of the 29th International Conference on Computational Linguistics

The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, we propose to boost the transferability of the stance detection model by using sentiment and commonsense knowledge, which are seldom considered in previous studies. Our model includes a graph autoencoder module to obtain commonsense knowledge and a stance detection module with sentiment and commonsense. Experimental results show that our model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark dataset–VAST. Meanwhile, ablation studies prove the significance of each module in our model. Analysis of the relations between sentiment, common sense, and stance indicates the effectiveness of sentiment and common sense.

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MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction
Yue Zhang | Zhenghua Li | Zuyi Bao | Jiacheng Li | Bo Zhang | Chen Li | Fei Huang | Min Zhang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7,063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources. Each sentence is corrected by three annotators, and their corrections are carefully reviewed by a senior annotator, resulting in 2.3 references per sentence. We conduct experiments with two mainstream CGEC models, i.e., the sequence-to-sequence model and the sequence-to-edit model, both enhanced with large pretrained language models, achieving competitive benchmark performance on previous and our datasets. We also discuss CGEC evaluation methodologies, including the effect of multiple references and using a char-based metric. Our annotation guidelines, data, and code are available at https://github.com/HillZhang1999/MuCGEC.

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Contrastive Data and Learning for Natural Language Processing
Rui Zhang | Yangfeng Ji | Yue Zhang | Rebecca J. Passonneau
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts

Current NLP models heavily rely on effective representation learning algorithms. Contrastive learning is one such technique to learn an embedding space such that similar data sample pairs have close representations while dissimilar samples stay far apart from each other. It can be used in supervised or unsupervised settings using different loss functions to produce task-specific or general-purpose representations. While it has originally enabled the success for vision tasks, recent years have seen a growing number of publications in contrastive NLP. This first line of works not only delivers promising performance improvements in various NLP tasks, but also provides desired characteristics such as task-agnostic sentence representation, faithful text generation, data-efficient learning in zero-shot and few-shot settings, interpretability and explainability. In this tutorial, we aim to provide a gentle introduction to the fundamentals of contrastive learning approaches and the theory behind them. We then survey the benefits and the best practices of contrastive learning for various downstream NLP applications including Text Classification, Question Answering, Summarization, Text Generation, Interpretability and Explainability, Commonsense Knowledge and Reasoning, Vision-and-Language.This tutorial intends to help researchers in the NLP and computational linguistics community to understand this emerging topic and promote future research directions of using contrastive learning for NLP applications.

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Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Yoav Goldberg | Zornitsa Kozareva | Yue Zhang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

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SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser
Yue Zhang | Bo Zhang | Zhenghua Li | Zuyi Bao | Chen Li | Min Zhang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

This work proposes a syntax-enhanced grammatical error correction (GEC) approach named SynGEC that effectively incorporates dependency syntactic information into the encoder part of GEC models. The key challenge for this idea is that off-the-shelf parsers are unreliable when processing ungrammatical sentences. To confront this challenge, we propose to build a tailored GEC-oriented parser (GOPar) using parallel GEC training data as a pivot. First, we design an extended syntax representation scheme that allows us to represent both grammatical errors and syntax in a unified tree structure. Then, we obtain parse trees of the source incorrect sentences by projecting trees of the target correct sentences. Finally, we train GOPar with such projected trees. For GEC, we employ the graph convolution network to encode source-side syntactic information produced by GOPar, and fuse them with the outputs of the Transformer encoder. Experiments on mainstream English and Chinese GEC datasets show that our proposed SynGEC approach consistently and substantially outperforms strong baselines and achieves competitive performance. Our code and data are all publicly available at https://github.com/HillZhang1999/SynGEC.

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Multi-Granularity Optimization for Non-Autoregressive Translation
Yafu Li | Leyang Cui | Yongjing Yin | Yue Zhang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Despite low latency, non-autoregressive machine translation (NAT) suffers severe performance deterioration due to the naive independence assumption. This assumption is further strengthened by cross-entropy loss, which encourages a strict match between the hypothesis and the reference token by token. To alleviate this issue, we propose multi-granularity optimization for NAT, which collects model behaviours on translation segments of various granularities and integrates feedback for backpropagation. Experiments on four WMT benchmarks show that the proposed method significantly outperforms the baseline models trained with cross-entropy loss, and achieves the best performance on WMT’16 En⇔Ro and highly competitive results on WMT’14 En⇔De for fully non-autoregressive translation.

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RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees
Tengxiao Liu | Qipeng Guo | Xiangkun Hu | Yue Zhang | Xipeng Qiu | Zheng Zhang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Interpreting the reasoning process from questions to answers poses a challenge in approaching explainable QA. A recently proposed structured reasoning format, entailment tree, manages to offer explicit logical deductions with entailment steps in a tree structure. To generate entailment trees, prior single pass sequence-to-sequence models lack visible internal decision probability, while stepwise approaches are supervised with extracted single step data and cannot model the tree as a whole. In this work, we propose RLET, a Reinforcement Learning based Entailment Tree generation framework, which is trained utilising the cumulative signals across the whole tree. RLET iteratively performs single step reasoning with sentence selection and deduction generation modules, from which the training signal is accumulated across the tree with elaborately designed aligned reward function that is consistent with the evaluation. To the best of our knowledge, we are the first to introduce RL into the entailment tree generation task. Experiments on three settings of the EntailmentBank dataset demonstrate the strength of using RL framework.

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Prompt-based Distribution Alignment for Domain Generalization in Text Classification
Chen Jia | Yue Zhang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Prompt-based learning (a.k.a. prompting) achieves high performance by bridging the gap between the objectives of language modeling and downstream tasks. Domain generalization ability can be improved by prompting since classification across different domains can be unified into the prediction of the same set of label words. The remaining challenge for domain generalization by prompting comes from discrepancies between the data distribution of different domains. To improve domain generalization with prompting, we learn distributional invariance across source domains via two alignment regularization loss functions. The first is vocabulary distribution alignment, which uses a Kullback-Leibler divergence regularization on source-domain vocabulary distributions. The second is feature distribution alignment, which uses a novel adversarial training strategy to learn domain invariant representation across source domains. Experiments on sentiment analysis and natural language inference show the effectiveness of our method and achieve state-of-the-art results on six datasets.

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Cross-domain Generalization for AMR Parsing
Xuefeng Bai | Sen Yang | Leyang Cui | Linfeng Song | Yue Zhang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. Recently, there has been notable growth in AMR parsing performance. However, most existing work focuses on improving the performance in the specific domain, ignoring the potential domain dependence of AMR parsing systems. To address this, we extensively evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain AMR parsing. We observe that challenges to cross-domain AMR parsing mainly arise from the distribution shift of words and AMR concepts. Based on our observation, we investigate two approaches to reduce the domain distribution divergence of text and AMR features, respectively. Experimental results on two out-of-domain test sets show the superiority of our method.

2021

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On Commonsense Cues in BERT for Solving Commonsense Tasks
Leyang Cui | Sijie Cheng | Yu Wu | Yue Zhang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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A Comparison between Pre-training and Large-scale Back-translation for Neural Machine Translation
Dandan Huang | Kun Wang | Yue Zhang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Template-Based Named Entity Recognition Using BART
Leyang Cui | Yu Wu | Jian Liu | Sen Yang | Yue Zhang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Generalized Supervised Attention for Text Generation
Yixian Liu | Liwen Zhang | Xinyu Zhang | Yong Jiang | Yue Zhang | Kewei Tu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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DialogSum: A Real-Life Scenario Dialogue Summarization Dataset
Yulong Chen | Yang Liu | Liang Chen | Yue Zhang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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What Did You Refer to? Evaluating Co-References in Dialogue
Wei-Nan Zhang | Yue Zhang | Hanlin Tang | Zhengyu Zhao | Caihai Zhu | Ting Liu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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DialogSum Challenge: Summarizing Real-Life Scenario Dialogues
Yulong Chen | Yang Liu | Yue Zhang
Proceedings of the 14th International Conference on Natural Language Generation

We propose a shared task on summarizing real-life scenario dialogues, DialogSum Challenge, to encourage researchers to address challenges in dialogue summarization, which has been less studied by the summarization community. Real-life scenario dialogue summarization has a wide potential application prospect in chat-bot and personal assistant. It contains unique challenges such as special discourse structure, coreference, pragmatics, and social common sense, which require specific representation learning technologies to deal with. We carefully annotate a large-scale dialogue summarization dataset based on multiple public dialogue corpus, opening the door to all kinds of summarization models.

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Towards Navigation by Reasoning over Spatial Configurations
Yue Zhang | Quan Guo | Parisa Kordjamshidi
Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics

We deal with the navigation problem where the agent follows natural language instructions while observing the environment. Focusing on language understanding, we show the importance of spatial semantics in grounding navigation instructions into visual perceptions. We propose a neural agent that uses the elements of spatial configurations and investigate their influence on the navigation agent’s reasoning ability. Moreover, we model the sequential execution order and align visual objects with spatial configurations in the instruction. Our neural agent improves strong baselines on the seen environments and shows competitive performance on the unseen environments. Additionally, the experimental results demonstrate that explicit modeling of spatial semantic elements in the instructions can improve the grounding and spatial reasoning of the model.

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Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation
Leonardo F. R. Ribeiro | Jonas Pfeiffer | Yue Zhang | Iryna Gurevych
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR. However, this assumes a high quality of generated AMRs, potentially limiting the transferability to the target task. In this paper, we investigate different techniques for automatically generating AMR annotations, where we aim to study which source of information yields better multilingual results. Our models trained on gold AMR with silver (machine translated) sentences outperform approaches which leverage generated silver AMR. We find that combining both complementary sources of information further improves multilingual AMR-to-text generation. Our models surpass the previous state of the art for German, Italian, Spanish, and Chinese by a large margin.

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Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation
Leyang Cui | Yu Wu | Shujie Liu | Yue Zhang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Although pre-training models have achieved great success in dialogue generation, their performance drops dramatically when the input contains an entity that does not appear in pre-training and fine-tuning datasets (unseen entity). To address this issue, existing methods leverage an external knowledge base to generate appropriate responses. In real-world practical, the entity may not be included by the knowledge base or suffer from the precision of knowledge retrieval. To deal with this problem, instead of introducing knowledge base as the input, we force the model to learn a better semantic representation by predicting the information in the knowledge base, only based on the input context. Specifically, with the help of a knowledge base, we introduce two auxiliary training objectives: 1) Interpret Masked Word, which conjectures the meaning of the masked entity given the context; 2) Hypernym Generation, which predicts the hypernym of the entity based on the context. Experiment results on two dialogue corpus verify the effectiveness of our methods under both knowledge available and unavailable settings.

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Entity Relation Extraction as Dependency Parsing in Visually Rich Documents
Yue Zhang | Zhang Bo | Rui Wang | Junjie Cao | Chen Li | Zuyi Bao
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e.,semantic entity), while the relations in-between are largely unexplored. In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task. Being different from the original dependency parsing model which recognizes dependency relations between words, we identify relations between groups of words with layout information instead. We have compared different representations of the semantic entity, different VRD encoders, and different relation decoders. For the model training, we explore multi-task learning to combine entity labeling and relation extraction tasks; and for the evaluation, we conduct experiments on different datasets with filtering and augmentation. The results demonstrate that our proposed model achieves 65.96% F1 score on the FUNSD dataset. As for the real-world application, our model has been applied to the in-house customs data, achieving reliable performance in the production setting.

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Natural Language Processing Meets Quantum Physics: A Survey and Categorization
Sixuan Wu | Jian Li | Peng Zhang | Yue Zhang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recent research has investigated quantum NLP, designing algorithms that process natural language in quantum computers, and also quantum-inspired algorithms that improve NLP performance on classical computers. In this survey, we review representative methods at the intersection of NLP and quantum physics in the past ten years, categorizing them according to the use of quantum theory, the linguistic targets that are modeled, and the downstream application. The literature review ends with a discussion on the key factors to the success that has been achieved by existing work, as well as challenges ahead, with the goal of better understanding the promises and further directions.

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Structural Adapters in Pretrained Language Models for AMR-to-Text Generation
Leonardo F. R. Ribeiro | Yue Zhang | Iryna Gurevych
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation. However, efficiently encoding the graph structure in PLMs is challenging because such models were pretrained on natural language, and modeling structured data may lead to catastrophic forgetting of distributional knowledge. In this paper, we propose StructAdapt, an adapter method to encode graph structure into PLMs. Contrary to prior work, StructAdapt effectively models interactions among the nodes based on the graph connectivity, only training graph structure-aware adapter parameters. In this way, we incorporate task-specific knowledge while maintaining the topological structure of the graph. We empirically show the benefits of explicitly encoding graph structure into PLMs using StructAdapt, outperforming the state of the art on two AMR-to-text datasets, training only 5.1% of the PLM parameters.

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Solving Aspect Category Sentiment Analysis as a Text Generation Task
Jian Liu | Zhiyang Teng | Leyang Cui | Hanmeng Liu | Yue Zhang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-trained representation. We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training. Experiments on several benchmarks show that our method gives the best reported results, having large advantages in few-shot and zero-shot settings.

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A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents
Qingrong Xia | Bo Zhang | Rui Wang | Zhenghua Li | Yue Zhang | Fei Huang | Luo Si | Min Zhang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of “Who expressed what opinions towards what” in one sentence. In this work, motivated by its span-based representations of opinion expressions and roles, we propose a unified span-based approach for the end-to-end OM setting. Furthermore, inspired by the unified span-based formalism of OM and constituent parsing, we explore two different methods (multi-task learning and graph convolutional neural network) to integrate syntactic constituents into the proposed model to help OM. We conduct experiments on the commonly used MPQA 2.0 dataset. The experimental results show that our proposed unified span-based approach achieves significant improvements over previous works in the exact F1 score and reduces the number of wrongly-predicted opinion expressions and roles, showing the effectiveness of our method. In addition, incorporating the syntactic constituents achieves promising improvements over the strong baseline enhanced by contextualized word representations.

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Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis
Linyi Yang | Jiazheng Li | Padraig Cunningham | Yue Zhang | Barry Smyth | Ruihai Dong
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data. Such issues come to be manifest in performance problems when faced with out-of-distribution data in the field. One recent solution has been to use counterfactually augmented datasets in order to reduce any reliance on spurious patterns that may exist in the original data. Producing high-quality augmented data can be costly and time-consuming as it usually needs to involve human feedback and crowdsourcing efforts. In this work, we propose an alternative by describing and evaluating an approach to automatically generating counterfactual data for the purpose of data augmentation and explanation. A comprehensive evaluation on several different datasets and using a variety of state-of-the-art benchmarks demonstrate how our approach can achieve significant improvements in model performance when compared to models training on the original data and even when compared to models trained with the benefit of human-generated augmented data.

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Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA?
Cunxiang Wang | Pai Liu | Yue Zhang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions. However, existing work is limited in using small benchmarks with high test-train overlaps. We construct a new dataset of closed-book QA using SQuAD, and investigate the performance of BART. Experiments show that it is challenging for BART to remember training facts in high precision, and also challenging to answer closed-book questions even if relevant knowledge is retained. Some promising directions are found, including decoupling the knowledge memorizing process and the QA finetune process, forcing the model to recall relevant knowledge when question answering.

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G-Transformer for Document-Level Machine Translation
Guangsheng Bao | Yue Zhang | Zhiyang Teng | Boxing Chen | Weihua Luo
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Document-level MT models are still far from satisfactory. Existing work extend translation unit from single sentence to multiple sentences. However, study shows that when we further enlarge the translation unit to a whole document, supervised training of Transformer can fail. In this paper, we find such failure is not caused by overfitting, but by sticking around local minima during training. Our analysis shows that the increased complexity of target-to-source attention is a reason for the failure. As a solution, we propose G-Transformer, introducing locality assumption as an inductive bias into Transformer, reducing the hypothesis space of the attention from target to source. Experiments show that G-Transformer converges faster and more stably than Transformer, achieving new state-of-the-art BLEU scores for both nonpretraining and pre-training settings on three benchmark datasets.

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End-to-End AMR Coreference Resolution
Qiankun Fu | Linfeng Song | Wenyu Du | Yue Zhang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Although parsing to Abstract Meaning Representation (AMR) has become very popular and AMR has been shown effective on the many sentence-level downstream tasks, little work has studied how to generate AMRs that can represent multi-sentence information. We introduce the first end-to-end AMR coreference resolution model in order to build multi-sentence AMRs. Compared with the previous pipeline and rule-based approaches, our model alleviates error propagation and it is more robust for both in-domain and out-domain situations. Besides, the document-level AMRs obtained by our model can significantly improve over the AMRs generated by a rule-based method (Liu et al., 2015) on text summarization.

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Semantic Representation for Dialogue Modeling
Xuefeng Bai | Yulong Chen | Linfeng Song | Yue Zhang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue modeling. Compared with the textual input, AMR explicitly provides core semantic knowledge and reduces data sparsity. We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems. Experimental results on both dialogue understanding and response generation tasks show the superiority of our model. To our knowledge, we are the first to leverage a formal semantic representation into neural dialogue modeling.

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On Compositional Generalization of Neural Machine Translation
Yafu Li | Yongjing Yin | Yulong Chen | Yue Zhang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT. However, there still exist significant issues such as robustness, domain generalization, etc. In this paper, we study NMT models from the perspective of compositional generalization by building a benchmark dataset, CoGnition, consisting of 216k clean and consistent sentence pairs. We quantitatively analyze effects of various factors using compound translation error rate, then demonstrate that the NMT model fails badly on compositional generalization, although it performs remarkably well under traditional metrics.

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Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter
Wei Liu | Xiyan Fu | Yue Zhang | Wenming Xiao
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Lexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labeling tasks due to their respective strengths. However, existing methods solely fuse lexicon features via a shallow and random initialized sequence layer and do not integrate them into the bottom layers of BERT. In this paper, we propose Lexicon Enhanced BERT (LEBERT) for Chinese sequence labeling, which integrates external lexicon knowledge into BERT layers directly by a Lexicon Adapter layer. Compared with existing methods, our model facilitates deep lexicon knowledge fusion at the lower layers of BERT. Experiments on ten Chinese datasets of three tasks including Named Entity Recognition, Word Segmentation, and Part-of-Speech Tagging, show that LEBERT achieves state-of-the-art results.

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TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
Xiao Wang | Qin Liu | Tao Gui | Qi Zhang | Yicheng Zou | Xin Zhou | Jiacheng Ye | Yongxin Zhang | Rui Zheng | Zexiong Pang | Qinzhuo Wu | Zhengyan Li | Chong Zhang | Ruotian Ma | Zichu Fei | Ruijian Cai | Jun Zhao | Xingwu Hu | Zhiheng Yan | Yiding Tan | Yuan Hu | Qiyuan Bian | Zhihua Liu | Shan Qin | Bolin Zhu | Xiaoyu Xing | Jinlan Fu | Yue Zhang | Minlong Peng | Xiaoqing Zheng | Yaqian Zhou | Zhongyu Wei | Xipeng Qiu | Xuanjing Huang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

TextFlint is a multilingual robustness evaluation toolkit for NLP tasks that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. This enables practitioners to automatically evaluate their models from various aspects or to customize their evaluations as desired with just a few lines of code. TextFlint also generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model in terms of its robustness. To guarantee acceptability, all the text transformations are linguistically based and all the transformed data selected (up to 100,000 texts) scored highly under human evaluation. To validate the utility, we performed large-scale empirical evaluations (over 67,000) on state-of-the-art deep learning models, classic supervised methods, and real-world systems. The toolkit is already available at https://github.com/textflint with all the evaluation results demonstrated at textflint.io.

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Learning to Rank in the Age of Muppets: Effectiveness–Efficiency Tradeoffs in Multi-Stage Ranking
Yue Zhang | ChengCheng Hu | Yuqi Liu | Hui Fang | Jimmy Lin
Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing

It is well known that rerankers built on pretrained transformer models such as BERT have dramatically improved retrieval effectiveness in many tasks. However, these gains have come at substantial costs in terms of efficiency, as noted by many researchers. In this work, we show that it is possible to retain the benefits of transformer-based rerankers in a multi-stage reranking pipeline by first using feature-based learning-to-rank techniques to reduce the number of candidate documents under consideration without adversely affecting their quality in terms of recall. Applied to the MS MARCO passage and document ranking tasks, we are able to achieve the same level of effectiveness, but with up to 18× increase in efficiency. Furthermore, our techniques are orthogonal to other methods focused on accelerating transformer inference, and thus can be combined for even greater efficiency gains. A higher-level message from our work is that, even though pretrained transformers dominate the modern IR landscape, there are still important roles for “traditional” LTR techniques, and that we should not forget history.

2020

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What Have We Achieved on Text Summarization?
Dandan Huang | Leyang Cui | Sen Yang | Guangsheng Bao | Kun Wang | Jun Xie | Yue Zhang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Deep learning has led to significant improvement in text summarization with various methods investigated and improved ROUGE scores reported over the years. However, gaps still exist between summaries produced by automatic summarizers and human professionals. Aiming to gain more understanding of summarization systems with respect to their strengths and limits on a fine-grained syntactic and semantic level, we consult the Multidimensional Quality Metric (MQM) and quantify 8 major sources of errors on 10 representative summarization models manually. Primarily, we find that 1) under similar settings, extractive summarizers are in general better than their abstractive counterparts thanks to strength in faithfulness and factual-consistency; 2) milestone techniques such as copy, coverage and hybrid extractive/abstractive methods do bring specific improvements but also demonstrate limitations; 3) pre-training techniques, and in particular sequence-to-sequence pre-training, are highly effective for improving text summarization, with BART giving the best results.

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Online Back-Parsing for AMR-to-Text Generation
Xuefeng Bai | Linfeng Song | Yue Zhang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

AMR-to-text generation aims to recover a text containing the same meaning as an input AMR graph. Current research develops increasingly powerful graph encoders to better represent AMR graphs, with decoders based on standard language modeling being used to generate outputs. We propose a decoder that back predicts projected AMR graphs on the target sentence during text generation. As the result, our outputs can better preserve the input meaning than standard decoders. Experiments on two AMR benchmarks show the superiority of our model over the previous state-of-the-art system based on graph Transformer.

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Inducing Target-Specific Latent Structures for Aspect Sentiment Classification
Chenhua Chen | Zhiyang Teng | Yue Zhang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Aspect-level sentiment analysis aims to recognize the sentiment polarity of an aspect or a target in a comment. Recently, graph convolutional networks based on linguistic dependency trees have been studied for this task. However, the dependency parsing accuracy of commercial product comments or tweets might be unsatisfactory. To tackle this problem, we associate linguistic dependency trees with automatically induced aspectspecific graphs. We propose gating mechanisms to dynamically combine information from word dependency graphs and latent graphs which are learned by self-attention networks. Our model can complement supervised syntactic features with latent semantic dependencies. Experimental results on five benchmarks show the effectiveness of our proposed latent models, giving significantly better results than models without using latent graphs.

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Coarse-to-Fine Pre-training for Named Entity Recognition
Xue Mengge | Bowen Yu | Zhenyu Zhang | Tingwen Liu | Yue Zhang | Bin Wang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

More recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT. However, currentpre-training techniques focus on building lan-guage modeling objectives to learn a gen-eral representation, ignoring the named entity-related knowledge. To this end, we proposea NER-specific pre-training framework to in-ject coarse-to-fine automatically mined entityknowledge into pre-trained models. Specifi-cally, we first warm-up the model via an en-tity span identification task by training it withWikipedia anchors, which can be deemed asgeneral-typed entities. Then we leverage thegazetteer-based distant supervision strategy totrain the model extract coarse-grained typedentities. Finally, we devise a self-supervisedauxiliary task to mine the fine-grained namedentity knowledge via clustering.Empiricalstudies on three public NER datasets demon-strate that our framework achieves significantimprovements against several pre-trained base-lines, establishing the new state-of-the-art per-formance on three benchmarks. Besides, weshow that our framework gains promising re-sults without using human-labeled trainingdata, demonstrating its effectiveness in label-few and low-resource scenarios.

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Entity Enhanced BERT Pre-training for Chinese NER
Chen Jia | Yuefeng Shi | Qinrong Yang | Yue Zhang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Character-level BERT pre-trained in Chinese suffers a limitation of lacking lexicon information, which shows effectiveness for Chinese NER. To integrate the lexicon into pre-trained LMs for Chinese NER, we investigate a semi-supervised entity enhanced BERT pre-training method. In particular, we first extract an entity lexicon from the relevant raw text using a new-word discovery method. We then integrate the entity information into BERT using Char-Entity-Transformer, which augments the self-attention using a combination of character and entity representations. In addition, an entity classification task helps inject the entity information into model parameters in pre-training. The pre-trained models are used for NER fine-tuning. Experiments on a news dataset and two datasets annotated by ourselves for NER in long-text show that our method is highly effective and achieves the best results.

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SemEval-2020 Task 4: Commonsense Validation and Explanation
Cunxiang Wang | Shuailong Liang | Yili Jin | Yilong Wang | Xiaodan Zhu | Yue Zhang
Proceedings of the Fourteenth Workshop on Semantic Evaluation

In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a natural language statement that makes sense to humans from one that does not, and provide the reasons. Specifically, in our first subtask, the participating systems are required to choose from two natural language statements of similar wording the one that makes sense and the one does not. The second subtask additionally asks a system to select the key reason from three options why a given statement does not make sense. In the third subtask, a participating system needs to generate the reason automatically. 39 teams submitted their valid systems to at least one subtask. For Subtask A and Subtask B, top-performing teams have achieved results closed to human performance. However, for Subtask C, there is still a considerable gap between system and human performance. The dataset used in our task can be found at https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation.

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Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
Leonardo F. R. Ribeiro | Yue Zhang | Claire Gardent | Iryna Gurevych
Transactions of the Association for Computational Linguistics, Volume 8

Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are directly connected. In contrast, local node encoding considers the relations between neighbor nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather both encoding strategies, proposing novel neural models that encode an input graph combining both global and local node contexts, in order to learn better contextualized node embeddings. In our experiments, we demonstrate that our approaches lead to significant improvements on two graph-to-text datasets achieving BLEU scores of 18.01 on the AGENDA dataset, and 63.69 on the WebNLG dataset for seen categories, outperforming state-of-the-art models by 3.7 and 3.1 points, respectively.1

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Enhancing Generalization in Natural Language Inference by Syntax
Qi He | Han Wang | Yue Zhang
Findings of the Association for Computational Linguistics: EMNLP 2020

Pre-trained language models such as BERT have achieved the state-of-the-art performance on natural language inference (NLI). However, it has been shown that such models can be tricked by variations of surface patterns such as syntax. We investigate the use of dependency trees to enhance the generalization of BERT in the NLI task, leveraging on a graph convolutional network to represent a syntax-based matching graph with heterogeneous matching patterns. Experimental results show that, our syntax-based method largely enhance generalization of BERT on a test set where the sentence pair has high lexical overlap but diverse syntactic structures, and do not degrade performance on the standard test set. In other words, the proposed method makes BERT more robust on syntactic changes.

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Multiscale Collaborative Deep Models for Neural Machine Translation
Xiangpeng Wei | Heng Yu | Yue Hu | Yue Zhang | Rongxiang Weng | Weihua Luo
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train. In this paper, we present a MultiScale Collaborative (MSC) framework to ease the training of NMT models that are substantially deeper than those used previously. We explicitly boost the gradient back-propagation from top to bottom levels by introducing a block-scale collaboration mechanism into deep NMT models. Then, instead of forcing the whole encoder stack directly learns a desired representation, we let each encoder block learns a fine-grained representation and enhance it by encoding spatial dependencies using a context-scale collaboration. We provide empirical evidence showing that the MSC nets are easy to optimize and can obtain improvements of translation quality from considerably increased depth. On IWSLT translation tasks with three translation directions, our extremely deep models (with 72-layer encoders) surpass strong baselines by +2.2 +3.1 BLEU points. In addition, our deep MSC achieves a BLEU score of 30.56 on WMT14 English-to-German task that significantly outperforms state-of-the-art deep NMT models. We have included the source code in supplementary materials.

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MuTual: A Dataset for Multi-Turn Dialogue Reasoning
Leyang Cui | Yu Wu | Shujie Liu | Yue Zhang | Ming Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Non-task oriented dialogue systems have achieved great success in recent years due to largely accessible conversation data and the development of deep learning techniques. Given a context, current systems are able to yield a relevant and fluent response, but sometimes make logical mistakes because of weak reasoning capabilities. To facilitate the conversation reasoning research, we introduce MuTual, a novel dataset for Multi-Turn dialogue Reasoning, consisting of 8,860 manually annotated dialogues based on Chinese student English listening comprehension exams. Compared to previous benchmarks for non-task oriented dialogue systems, MuTual is much more challenging since it requires a model that be able to handle various reasoning problems. Empirical results show that state-of-the-art methods only reach 71%, which is far behind human performance of 94%, indicating that there is ample room for improving reasoning ability.

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Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
Xiangyu Duan | Baijun Ji | Hao Jia | Min Tan | Min Zhang | Boxing Chen | Weihua Luo | Yue Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary. Motivated by the ability of a monolingual speaker learning to translate via looking up the bilingual dictionary, we propose the task to see how much potential an MT system can attain using the bilingual dictionary and large scale monolingual corpora, while is independent on parallel sentences. We propose anchored training (AT) to tackle the task. AT uses the bilingual dictionary to establish anchoring points for closing the gap between source language and target language. Experiments on various language pairs show that our approaches are significantly better than various baselines, including dictionary-based word-by-word translation, dictionary-supervised cross-lingual word embedding transformation, and unsupervised MT. On distant language pairs that are hard for unsupervised MT to perform well, AT performs remarkably better, achieving performances comparable to supervised SMT trained on more than 4M parallel sentences.

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Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks
Bo Zhang | Yue Zhang | Rui Wang | Zhenghua Li | Min Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Opinion role labeling (ORL) is a fine-grained opinion analysis task and aims to answer “who expressed what kind of sentiment towards what?”. Due to the scarcity of labeled data, ORL remains challenging for data-driven methods. In this work, we try to enhance neural ORL models with syntactic knowledge by comparing and integrating different representations. We also propose dependency graph convolutional networks (DEPGCN) to encode parser information at different processing levels. In order to compensate for parser inaccuracy and reduce error propagation, we introduce multi-task learning (MTL) to train the parser and the ORL model simultaneously. We verify our methods on the benchmark MPQA corpus. The experimental results show that syntactic information is highly valuable for ORL, and our final MTL model effectively boosts the F1 score by 9.29 over the syntax-agnostic baseline. In addition, we find that the contributions from syntactic knowledge do not fully overlap with contextualized word representations (BERT). Our best model achieves 4.34 higher F1 score than the current state-ofthe-art.

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AMR Parsing with Latent Structural Information
Qiji Zhou | Yue Zhang | Donghong Ji | Hao Tang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences. We investigate parsing AMR with explicit dependency structures and interpretable latent structures. We generate the latent soft structure without additional annotations, and fuse both dependency and latent structure via an extended graph neural networks. The fused structural information helps our experiments results to achieve the best reported results on both AMR 2.0 (77.5% Smatch F1 on LDC2017T10) and AMR 1.0 ((71.8% Smatch F1 on LDC2014T12).

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ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT
Linfeng Song | Kun Xu | Yue Zhang | Jianshu Chen | Dong Yu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Zero pronoun recovery and resolution aim at recovering the dropped pronoun and pointing out its anaphoric mentions, respectively. We propose to better explore their interaction by solving both tasks together, while the previous work treats them separately. For zero pronoun resolution, we study this task in a more realistic setting, where no parsing trees or only automatic trees are available, while most previous work assumes gold trees. Experiments on two benchmarks show that joint modeling significantly outperforms our baseline that already beats the previous state of the arts.

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Multi-Cell Compositional LSTM for NER Domain Adaptation
Chen Jia | Yue Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Cross-domain NER is a challenging yet practical problem. Entity mentions can be highly different across domains. However, the correlations between entity types can be relatively more stable across domains. We investigate a multi-cell compositional LSTM structure for multi-task learning, modeling each entity type using a separate cell state. With the help of entity typed units, cross-domain knowledge transfer can be made in an entity type level. Theoretically, the resulting distinct feature distributions for each entity type make it more powerful for cross-domain transfer. Empirically, experiments on four few-shot and zero-shot datasets show our method significantly outperforms a series of multi-task learning methods and achieves the best results.

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Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
Libo Qin | Xiao Xu | Wanxiang Che | Yue Zhang | Ting Liu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a certain number of task domains, such as navigation and scheduling. This makes it difficult to scalable for a new domain with limited labeled data. However, there has been relatively little research on how to effectively use data from all domains to improve the performance of each domain and also unseen domains. To this end, we investigate methods that can make explicit use of domain knowledge and introduce a shared-private network to learn shared and specific knowledge. In addition, we propose a novel Dynamic Fusion Network (DF-Net) which automatically exploit the relevance between the target domain and each domain. Results show that our models outperforms existing methods on multi-domain dialogue, giving the state-of-the-art in the literature. Besides, with little training data, we show its transferability by outperforming prior best model by 13.9% on average.

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Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
Wenyu Du | Zhouhan Lin | Yikang Shen | Timothy J. O’Donnell | Yoshua Bengio | Yue Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structure into neural language models has been a challenging topic. In this paper, we make use of a multi-task objective, i.e., the models simultaneously predict words as well as ground truth parse trees in a form called “syntactic distances”, where information between these two separate objectives shares the same intermediate representation. Experimental results on the Penn Treebank and Chinese Treebank datasets show that when ground truth parse trees are provided as additional training signals, the model is able to achieve lower perplexity and induce trees with better quality.

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DRTS Parsing with Structure-Aware Encoding and Decoding
Qiankun Fu | Yue Zhang | Jiangming Liu | Meishan Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently. State-of-the-art performance can be achieved by a neural sequence-to-sequence model, treating the tree construction as an incremental sequence generation problem. Structural information such as input syntax and the intermediate skeleton of the partial output has been ignored in the model, which could be potentially useful for the DRTS parsing. In this work, we propose a structural-aware model at both the encoder and decoder phase to integrate the structural information, where graph attention network (GAT) is exploited for effectively modeling. Experimental results on a benchmark dataset show that our proposed model is effective and can obtain the best performance in the literature.

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Structural Information Preserving for Graph-to-Text Generation
Linfeng Song | Ante Wang | Jinsong Su | Yue Zhang | Kun Xu | Yubin Ge | Dong Yu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs. We propose to tackle this problem by leveraging richer training signals that can guide our model for preserving input information. In particular, we introduce two types of autoencoding losses, each individually focusing on different aspects (a.k.a. views) of input graphs. The losses are then back-propagated to better calibrate our model via multi-task training. Experiments on two benchmarks for graph-to-text generation show the effectiveness of our approach over a state-of-the-art baseline.

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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts
Agata Savary | Yue Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

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Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset
Edwin Zhang | Nikhil Gupta | Raphael Tang | Xiao Han | Ronak Pradeep | Kuang Lu | Yue Zhang | Rodrigo Nogueira | Kyunghyun Cho | Hui Fang | Jimmy Lin
Proceedings of the First Workshop on Scholarly Document Processing

We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. Our system has been online and serving users since late March 2020. The Covidex is the user application component of our three-pronged strategy to develop technologies for helping domain experts tackle the ongoing global pandemic. In addition, we provide robust and easy-to-use keyword search infrastructure that exploits mature fusion-based methods as well as standalone neural ranking models that can be incorporated into other applications. These techniques have been evaluated in the multi-round TREC-COVID challenge: Our infrastructure and baselines have been adopted by many participants, including some of the best systems. In round 3, we submitted the highest-scoring run that took advantage of previous training data and the second-highest fully automatic run. In rounds 4 and 5, we submitted the highest-scoring fully automatic runs.

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Investigating Rich Feature Sources for Conceptual Representation Encoding
Lu Cao | Yulong Chen | Dandan Huang | Yue Zhang
Proceedings of the Workshop on the Cognitive Aspects of the Lexicon

Functional Magnetic Resonance Imaging (fMRI) provides a means to investigate human conceptual representation in cognitive and neuroscience studies, where researchers predict the fMRI activations with elicited stimuli inputs. Previous work mainly uses a single source of features, particularly linguistic features, to predict fMRI activations. However, relatively little work has been done on investigating rich-source features for conceptual representation. In this paper, we systematically compare the linguistic, visual as well as auditory input features in conceptual representation, and further introduce associative conceptual features, which are obtained from Small World of Words game, to predict fMRI activations. Our experimental results show that those rich-source features can enhance performance in predicting the fMRI activations. Our analysis indicates that information from rich sources is present in the conceptual representation of human brains. In particular, the visual feature weights the most on conceptual representation, which is consistent with the recent cognitive science study.

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Proceedings of the 19th Chinese National Conference on Computational Linguistics
Maosong Sun (孙茂松) | Sujian Li (李素建) | Yue Zhang (张岳) | Yang Liu (刘洋)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

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新型冠状病毒肺炎相关的推特主题与情感研究(Exploring COVID-19-related Twitter Topic Dynamics across Countries)
Shuailong Liang (梁帅龙) | Derek F. Wong (黄辉) | Yue Zhang (张岳)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

我们基于从2020年1月22日至2020年4月30日在推特社交平台上抓取的不同国家和地区发布的50万条推文,研究了有关 2019新型冠状病毒肺炎相关的主题和人们的观点,发现了不同国家之间推特用户的普遍关切和看法之间存在着异同,并且对不同议题的情感态度也有所不同。我们发现大部分推文中包含了强烈的情感,其中表达爱与支持的推文比较普遍。总体来看,人们的情感随着时间的推移逐渐正向增强。

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Cross-Lingual Dependency Parsing via Self-Training
Meishan Zhang | Yue Zhang
Proceedings of the 19th Chinese National Conference on Computational Linguistics

Recent advances of multilingual word representations weaken the input divergences across languages, making cross-lingual transfer similar to the monolingual cross-domain and semi-supervised settings. Thus self-training, which is effective for these settings, could be possibly beneficial to cross-lingual as well. This paper presents the first comprehensive study for self-training in cross-lingual dependency parsing. Three instance selection strategies are investigated, where two of which are based on the baseline dependency parsing model, and the third one adopts an auxiliary cross-lingual POS tagging model as evidence. We conduct experiments on the universal dependencies for eleven languages. Results show that self-training can boost the dependency parsing performances on the target languages. In addition, the POS tagger assistant instance selection can achieve further improvements consistently. Detailed analysis is conducted to examine the potentiality of self-training in-depth.

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Making the Best Use of Review Summary for Sentiment Analysis
Sen Yang | Leyang Cui | Jun Xie | Yue Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Sentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may give additional benefit for review sentiment analysis. In this paper, we conduct a study to exploit methods for better use of summary information. We start by finding out that the sentimental signal distribution of a review and that of its corresponding summary are in fact complementary to each other. We thus explore various architectures to better guide the interactions between the two and propose a hierarchically-refined review-centric attention model. Empirical results show that our review-centric model can make better use of user-written summaries for review sentiment analysis, and is also more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.

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Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets
Frank Xing | Lorenzo Malandri | Yue Zhang | Erik Cambria
Proceedings of the 28th International Conference on Computational Linguistics

The recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors. Interpretability, however, is a critical requirement for many downstream AI and NLP applications, e.g., in finance, healthcare, and autonomous driving. This study, instead of proposing any “new model”, investigates the error patterns of some widely acknowledged sentiment analysis methods in the finance domain. We discover that (1) those methods belonging to the same clusters are prone to similar error patterns, and (2) there are six types of linguistic features that are pervasive in the common errors. These findings provide important clues and practical considerations for improving sentiment analysis models for financial applications.

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Sentiment Forecasting in Dialog
Zhongqing Wang | Xiujun Zhu | Yue Zhang | Shoushan Li | Guodong Zhou
Proceedings of the 28th International Conference on Computational Linguistics

Sentiment forecasting in dialog aims to predict the polarity of next utterance to come, and can help speakers revise their utterances in sentimental utterances generation. However, the polarity of next utterance is normally hard to predict, due to the lack of content of next utterance (yet to come). In this study, we propose a Neural Sentiment Forecasting (NSF) model to address inherent challenges. In particular, we employ a neural simulation model to simulate the next utterance based on the context (previous utterances encountered). Moreover, we employ a sequence influence model to learn both pair-wise and seq-wise influence. Empirical studies illustrate the importance of proposed sentiment forecasting task, and justify the effectiveness of our NSF model over several strong baselines.

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Does Chinese BERT Encode Word Structure?
Yile Wang | Leyang Cui | Yue Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Contextualized representations give significantly improved results for a wide range of NLP tasks. Much work has been dedicated to analyzing the features captured by representative models such as BERT. Existing work finds that syntactic, semantic and word sense knowledge are encoded in BERT. However, little work has investigated word features for character languages such as Chinese. We investigate Chinese BERT using both attention weight distribution statistics and probing tasks, finding that (1) word information is captured by BERT; (2) word-level features are mostly in the middle representation layers; (3) downstream tasks make different use of word features in BERT, with POS tagging and chunking relying the most on word features, and natural language inference relying the least on such features.

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Semantic Role Labeling with Heterogeneous Syntactic Knowledge
Qingrong Xia | Rui Wang | Zhenghua Li | Yue Zhang | Min Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention. Most of the previous syntax-aware SRL works focus on explicitly modeling homogeneous syntactic knowledge over tree outputs. In this work, we propose to encode heterogeneous syntactic knowledge for SRL from both explicit and implicit representations. First, we introduce graph convolutional networks to explicitly encode multiple heterogeneous dependency parse trees. Second, we extract the implicit syntactic representations from syntactic parser trained with heterogeneous treebanks. Finally, we inject the two types of heterogeneous syntax-aware representations into the base SRL model as extra inputs. We conduct experiments on two widely-used benchmark datasets, i.e., Chinese Proposition Bank 1.0 and English CoNLL-2005 dataset. Experimental results show that incorporating heterogeneous syntactic knowledge brings significant improvements over strong baselines. We further conduct detailed analysis to gain insights on the usefulness of heterogeneous (vs. homogeneous) syntactic knowledge and the effectiveness of our proposed approaches for modeling such knowledge.

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Porous Lattice Transformer Encoder for Chinese NER
Xue Mengge | Bowen Yu | Tingwen Liu | Yue Zhang | Erli Meng | Bin Wang
Proceedings of the 28th International Conference on Computational Linguistics

Incorporating lexicons into character-level Chinese NER by lattices is proven effective to exploitrich word boundary information. Previous work has extended RNNs to consume lattice inputsand achieved great success. However, due to the DAG structure and the inherently unidirectionalsequential nature, this method precludes batched computation and sufficient semantic interaction. In this paper, we propose PLTE, an extension of transformer encoder that is tailored for ChineseNER, which models all the characters and matched lexical words in parallel with batch process-ing. PLTE augments self-attention with positional relation representations to incorporate latticestructure. It also introduces a porous mechanism to augment localness modeling and maintainthe strength of capturing the rich long-term dependencies. Experimental results show that PLTEperforms up to 11.4 times faster than state-of-the-art methods while realizing better performance. We also demonstrate that using BERT representations further substantially boosts the performanceand brings out the best in PLTE.

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Layer-Wise Multi-View Learning for Neural Machine Translation
Qiang Wang | Changliang Li | Yue Zhang | Tong Xiao | Jingbo Zhu
Proceedings of the 28th International Conference on Computational Linguistics

Traditional neural machine translation is limited to the topmost encoder layer’s context representation and cannot directly perceive the lower encoder layers. Existing solutions usually rely on the adjustment of network architecture, making the calculation more complicated or introducing additional structural restrictions. In this work, we propose layer-wise multi-view learning to solve this problem, circumventing the necessity to change the model structure. We regard each encoder layer’s off-the-shelf output, a by-product in layer-by-layer encoding, as the redundant view for the input sentence. In this way, in addition to the topmost encoder layer (referred to as the primary view), we also incorporate an intermediate encoder layer as the auxiliary view. We feed the two views to a partially shared decoder to maintain independent prediction. Consistency regularization based on KL divergence is used to encourage the two views to learn from each other. Extensive experimental results on five translation tasks show that our approach yields stable improvements over multiple strong baselines. As another bonus, our method is agnostic to network architectures and can maintain the same inference speed as the original model.

2019

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Cross-Domain NER using Cross-Domain Language Modeling
Chen Jia | Xiaobo Liang | Yue Zhang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Due to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both the source and target domains. A disadvantage of such methods is that they cannot train for domains without NER data. To address this issue, we consider using cross-domain LM as a bridge cross-domains for NER domain adaptation, performing cross-domain and cross-task knowledge transfer by designing a novel parameter generation network. Results show that our method can effectively extract domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while also giving state-of-the-art results among supervised domain adaptation methods.

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Open Domain Event Extraction Using Neural Latent Variable Models
Xiao Liu | Heyan Huang | Yue Zhang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We consider open domain event extraction, the task of extracting unconstraint types of events from news clusters. A novel latent variable neural model is constructed, which is scalable to very large corpus. A dataset is collected and manually annotated, with task-specific evaluation metrics being designed. Results show that the proposed unsupervised model gives better performance compared to the state-of-the-art method for event schema induction.

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Leveraging Local and Global Patterns for Self-Attention Networks
Mingzhou Xu | Derek F. Wong | Baosong Yang | Yue Zhang | Lidia S. Chao
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Self-attention networks have received increasing research attention. By default, the hidden states of each word are hierarchically calculated by attending to all words in the sentence, which assembles global information. However, several studies pointed out that taking all signals into account may lead to overlooking neighboring information (e.g. phrase pattern). To address this argument, we propose a hybrid attention mechanism to dynamically leverage both of the local and global information. Specifically, our approach uses a gating scalar for integrating both sources of the information, which is also convenient for quantifying their contributions. Experiments on various neural machine translation tasks demonstrate the effectiveness of the proposed method. The extensive analyses verify that the two types of contexts are complementary to each other, and our method gives highly effective improvements in their integration.

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Tree Communication Models for Sentiment Analysis
Yuan Zhang | Yue Zhang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Tree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be calculated simultaneously. However, traditional tree-LSTMs capture only the bottom-up dependencies between constituents. In this paper, we propose a tree communication model using graph convolutional neural network and graph recurrent neural network, which allows rich information exchange between phrases constituent tree. Experiments show that our model outperforms existing work on bidirectional tree-LSTMs in both accuracy and efficiency, providing more consistent predictions on phrase-level sentiments.

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Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation
Cunxiang Wang | Shuailong Liang | Yue Zhang | Xiaonan Li | Tian Gao
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Introducing common sense to natural language understanding systems has received increasing research attention. It remains a fundamental question on how to evaluate whether a system has the sense-making capability. Existing benchmarks measure common sense knowledge indirectly or without reasoning. In this paper, we release a benchmark to directly test whether a system can differentiate natural language statements that make sense from those that do not make sense. In addition, a system is asked to identify the most crucial reason why a statement does not make sense. We evaluate models trained over large-scale language modeling tasks as well as human performance, showing that there are different challenges for system sense-making.

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Latent Variable Sentiment Grammar
Liwen Zhang | Kewei Tu | Yue Zhang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Neural models have been investigated for sentiment classification over constituent trees. They learn phrase composition automatically by encoding tree structures but do not explicitly model sentiment composition, which requires to encode sentiment class labels. To this end, we investigate two formalisms with deep sentiment representations that capture sentiment subtype expressions by latent variables and Gaussian mixture vectors, respectively. Experiments on Stanford Sentiment Treebank (SST) show the effectiveness of sentiment grammar over vanilla neural encoders. Using ELMo embeddings, our method gives the best results on this benchmark.

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Semantic Neural Machine Translation Using AMR
Linfeng Song | Daniel Gildea | Yue Zhang | Zhiguo Wang | Jinsong Su
Transactions of the Association for Computational Linguistics, Volume 7

It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.

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SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT
Yue Zhang | Wei Jiang | Qingrong Xia | Junjie Cao | Rui Wang | Zhenghua Li | Min Zhang
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

In this paper, we describe our participating systems in the shared task on Cross- Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). The task includes five frameworks for graph-based meaning representations, i.e., DM, PSD, EDS, UCCA, and AMR. One common characteristic of our systems is that we employ graph-based methods instead of transition-based methods when predicting edges between nodes. For SDP, we jointly perform edge prediction, frame tagging, and POS tagging via multi-task learning (MTL). For UCCA, we also jointly model a constituent tree parsing and a remote edge recovery task. For both EDS and AMR, we produce nodes first and edges second in a pipeline fashion. External resources like BERT are found helpful for all frameworks except AMR. Our final submission ranks the third on the overall MRP evaluation metric, the first on EDS and the second on UCCA.

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Code-Switching for Enhancing NMT with Pre-Specified Translation
Kai Song | Yue Zhang | Heng Yu | Weihua Luo | Kun Wang | Min Zhang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags for lexicon words and the use of hard constraints during decoding. Both methods can hurt translation fidelity for various reasons. We investigate a data augmentation method, making code-switched training data by replacing source phrases with their target translations. Our method does not change the MNT model or decoding algorithm, allowing the model to learn lexicon translations by copying source-side target words. Extensive experiments show that our method achieves consistent improvements over existing approaches, improving translation of constrained words without hurting unconstrained words.

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Subword Encoding in Lattice LSTM for Chinese Word Segmentation
Jie Yang | Yue Zhang | Shuailong Liang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We investigate subword information for Chinese word segmentation, by integrating sub word embeddings trained using byte-pair encoding into a Lattice LSTM (LaLSTM) network over a character sequence. Experiments on standard benchmark show that subword information brings significant gains over strong character-based segmentation models. To our knowledge, this is the first research on the effectiveness of subwords on neural word segmentation.

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Improving Cross-Domain Chinese Word Segmentation with Word Embeddings
Yuxiao Ye | Weikang Li | Yue Zhang | Likun Qiu | Jian Sun
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS. The limited amount of annotated data in the target domain has been the key obstacle to a satisfactory performance. In this paper, we propose a semi-supervised word-based approach to improving cross-domain CWS given a baseline segmenter. Particularly, our model only deploys word embeddings trained on raw text in the target domain, discarding complex hand-crafted features and domain-specific dictionaries. Innovative subsampling and negative sampling methods are proposed to derive word embeddings optimized for CWS. We conduct experiments on five datasets in special domains, covering domains in novels, medicine, and patent. Results show that our model can obviously improve cross-domain CWS, especially in the segmentation of domain-specific noun entities. The word F-measure increases by over 3.0% on four datasets, outperforming state-of-the-art semi-supervised and unsupervised cross-domain CWS approaches with a large margin. We make our data and code available on Github.

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Leveraging Dependency Forest for Neural Medical Relation Extraction
Linfeng Song | Yue Zhang | Daniel Gildea | Mo Yu | Zhiguo Wang | Jinsong Su
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Medical relation extraction discovers relations between entity mentions in text, such as research articles. For this task, dependency syntax has been recognized as a crucial source of features. Yet in the medical domain, 1-best parse trees suffer from relatively low accuracies, diminishing their usefulness. We investigate a method to alleviate this problem by utilizing dependency forests. Forests contain more than one possible decisions and therefore have higher recall but more noise compared with 1-best outputs. A graph neural network is used to represent the forests, automatically distinguishing the useful syntactic information from parsing noise. Results on two benchmarks show that our method outperforms the standard tree-based methods, giving the state-of-the-art results in the literature.

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Syntax-Enhanced Self-Attention-Based Semantic Role Labeling
Yue Zhang | Rui Wang | Luo Si
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence. This paper investigates how to incorporate syntactic knowledge into the SRL task effectively. We present different approaches of en- coding the syntactic information derived from dependency trees of different quality and representations; we propose a syntax-enhanced self-attention model and compare it with other two strong baseline methods; and we con- duct experiments with newly published deep contextualized word representations as well. The experiment results demonstrate that with proper incorporation of the high quality syntactic information, our model achieves a new state-of-the-art performance for the Chinese SRL task on the CoNLL-2009 dataset.

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Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank
Meishan Zhang | Yue Zhang | Guohong Fu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Treebank translation is a promising method for cross-lingual transfer of syntactic dependency knowledge. The basic idea is to map dependency arcs from a source treebank to its target translation according to word alignments. This method, however, can suffer from imperfect alignment between source and target words. To address this problem, we investigate syntactic transfer by code mixing, translating only confident words in a source treebank. Cross-lingual word embeddings are leveraged for transferring syntactic knowledge to the target from the resulting code-mixed treebank. Experiments on University Dependency Treebanks show that code-mixed treebanks are more effective than translated treebanks, giving highly competitive performances among cross-lingual parsing methods.

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Contrastive Attention Mechanism for Abstractive Sentence Summarization
Xiangyu Duan | Hongfei Yu | Mingming Yin | Min Zhang | Weihua Luo | Yue Zhang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence. The proposed contrastive attention mechanism accommodates two categories of attention: one is the conventional attention that attends to relevant parts of the source sentence, the other is the opponent attention that attends to irrelevant or less relevant parts of the source sentence. Both attentions are trained in an opposite way so that the contribution from the conventional attention is encouraged and the contribution from the opponent attention is discouraged through a novel softmax and softmin functionality. Experiments on benchmark datasets show that, the proposed contrastive attention mechanism is more focused on the relevant parts for the summary than the conventional attention mechanism, and greatly advances the state-of-the-art performance on the abstractive sentence summarization task. We release the code at https://github.com/travel-go/ Abstractive-Text-Summarization.

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A Pilot Study for Chinese SQL Semantic Parsing
Qingkai Min | Yuefeng Shi | Yue Zhang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The task of semantic parsing is highly useful for dialogue and question answering systems. Many datasets have been proposed to map natural language text into SQL, among which the recent Spider dataset provides cross-domain samples with multiple tables and complex queries. We build a Spider dataset for Chinese, which is currently a low-resource language in this task area. Interesting research questions arise from the uniqueness of the language, which requires word segmentation, and also from the fact that SQL keywords and columns of DB tables are typically written in English. We compare character- and word-based encoders for a semantic parser, and different embedding schemes. Results show that word-based semantic parser is subject to segmentation errors and cross-lingual word embeddings are useful for text-to-SQL.

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Hierarchically-Refined Label Attention Network for Sequence Labeling
Leyang Cui | Yue Zhang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

CRF has been used as a powerful model for statistical sequence labeling. For neural sequence labeling, however, BiLSTM-CRF does not always lead to better results compared with BiLSTM-softmax local classification. This can be because the simple Markov label transition model of CRF does not give much information gain over strong neural encoding. For better representing label sequences, we investigate a hierarchically-refined label attention network, which explicitly leverages label embeddings and captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention. Results on POS tagging, NER and CCG supertagging show that the proposed model not only improves the overall tagging accuracy with similar number of parameters, but also significantly speeds up the training and testing compared to BiLSTM-CRF.

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Multi-Granular Text Encoding for Self-Explaining Categorization
Zhiguo Wang | Yue Zhang | Mo Yu | Wei Zhang | Lin Pan | Linfeng Song | Kun Xu | Yousef El-Kurdi
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Self-explaining text categorization requires a classifier to make a prediction along with supporting evidence. A popular type of evidence is sub-sequences extracted from the input text which are sufficient for the classifier to make the prediction. In this work, we define multi-granular ngrams as basic units for explanation, and organize all ngrams into a hierarchical structure, so that shorter ngrams can be reused while computing longer ngrams. We leverage the tree-structured LSTM to learn a context-independent representation for each unit via parameter sharing. Experiments on medical disease classification show that our model is more accurate, efficient and compact than the BiLSTM and CNN baselines. More importantly, our model can extract intuitive multi-granular evidence to support its predictions.

2018

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Neural Transition-based Syntactic Linearization
Linfeng Song | Yue Zhang | Daniel Gildea
Proceedings of the 11th International Conference on Natural Language Generation

The task of linearization is to find a grammatical order given a set of words. Traditional models use statistical methods. Syntactic linearization systems, which generate a sentence along with its syntactic tree, have shown state-of-the-art performance. Recent work shows that a multilayer LSTM language model outperforms competitive statistical syntactic linearization systems without using syntax. In this paper, we study neural syntactic linearization, building a transition-based syntactic linearizer leveraging a feed forward neural network, observing significantly better results compared to LSTM language models on this task.

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Two Local Models for Neural Constituent Parsing
Zhiyang Teng | Yue Zhang
Proceedings of the 27th International Conference on Computational Linguistics

Non-local features have been exploited by syntactic parsers for capturing dependencies between sub output structures. Such features have been a key to the success of state-of-the-art statistical parsers. With the rise of deep learning, however, it has been shown that local output decisions can give highly competitive accuracies, thanks to the power of dense neural input representations that embody global syntactic information. We investigate two conceptually simple local neural models for constituent parsing, which make local decisions to constituent spans and CFG rules, respectively. Consistent with previous findings along the line, our best model gives highly competitive results, achieving the labeled bracketing F1 scores of 92.4% on PTB and 87.3% on CTB 5.1.

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Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction
Junwen Duan | Yue Zhang | Xiao Ding | Ching-Yun Chang | Ting Liu
Proceedings of the 27th International Conference on Computational Linguistics

Texts from the Internet serve as important data sources for financial market modeling. Early statistical approaches rely on manually defined features to capture lexical, sentiment and event information, which suffers from feature sparsity. Recent work has considered learning dense representations for news titles and abstracts. Compared to news titles, full documents can contain more potentially helpful information, but also noise compared to events and sentences, which has been less investigated in previous work. To fill this gap, we propose a novel target-specific abstract-guided news document representation model. The model uses a target-sensitive representation of the news abstract to weigh sentences in the news content, so as to select and combine the most informative sentences for market modeling. Results show that document representations can give better performance for estimating cumulative abnormal returns of companies when compared to titles and abstracts. Our model is especially effective when it used to combine information from multiple document sources compared to the sentence-level baselines.

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Design Challenges and Misconceptions in Neural Sequence Labeling
Jie Yang | Shuailong Liang | Yue Zhang
Proceedings of the 27th International Conference on Computational Linguistics

We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.

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Cross-lingual Terminology Extraction for Translation Quality Estimation
Yu Yuan | Yuze Gao | Yue Zhang | Serge Sharoff
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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N-ary Relation Extraction using Graph-State LSTM
Linfeng Song | Yue Zhang | Zhiguo Wang | Daniel Gildea
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Cross-sentence n-ary relation extraction detects relations among n entities across multiple sentences. Typical methods formulate an input as a document graph, integrating various intra-sentential and inter-sentential dependencies. The current state-of-the-art method splits the input graph into two DAGs, adopting a DAG-structured LSTM for each. Though being able to model rich linguistic knowledge by leveraging graph edges, important information can be lost in the splitting procedure. We propose a graph-state LSTM model, which uses a parallel state to model each word, recurrently enriching state values via message passing. Compared with DAG LSTMs, our graph LSTM keeps the original graph structure, and speeds up computation by allowing more parallelization. On a standard benchmark, our model shows the best result in the literature.

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Joint models for NLP
Yue Zhang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Joint models have received much research attention in NLP, allowing relevant tasks to share common information while avoiding error propagation in multi-stage pepelines. Several main approaches have been taken by statistical joint modeling, while neural models allow parameter sharing and adversarial training. This tutorial reviews main approaches to joint modeling for both statistical and neural methods.

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Learning Domain Representation for Multi-Domain Sentiment Classification
Qi Liu | Yue Zhang | Jiangming Liu
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Training data for sentiment analysis are abundant in multiple domains, yet scarce for other domains. It is useful to leveraging data available for all existing domains to enhance performance on different domains. We investigate this problem by learning domain-specific representations of input sentences using neural network. In particular, a descriptor vector is learned for representing each domain, which is used to map adversarially trained domain-general Bi-LSTM input representations into domain-specific representations. Based on this model, we further expand the input representation with exemplary domain knowledge, collected by attending over a memory network of domain training data. Results show that our model outperforms existing methods on multi-domain sentiment analysis significantly, giving the best accuracies on two different benchmarks.

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Mining Evidences for Concept Stock Recommendation
Qi Liu | Yue Zhang
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We investigate the task of mining relevant stocks given a topic of concern on emerging capital markets, for which there is lack of structural understanding. Deep learning is leveraged to mine evidences from large scale textual data, which contain valuable market information. In particular, distributed word similarities trained over large scale raw texts are taken as a basis of relevance measuring, and deep reinforcement learning is leveraged to learn a strategy of topic expansion, given a small amount of manually labeled data from financial analysts. Results on two Chinese stock market datasets show that our method outperforms a strong baseline using information retrieval techniques.

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Leveraging Context Information for Natural Question Generation
Linfeng Song | Zhiguo Wang | Wael Hamza | Yue Zhang | Daniel Gildea
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer. It is useful for enlarging the training set of QA systems. Previous work has adopted sequence-to-sequence models that take a passage with an additional bit to indicate answer position as input. However, they do not explicitly model the information between answer and other context within the passage. We propose a model that matches the answer with the passage before generating the question. Experiments show that our model outperforms the existing state of the art using rich features.

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Sentence-State LSTM for Text Representation
Yue Zhang | Qi Liu | Linfeng Song
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform local and global information exchange between words simultaneously, rather than incremental reading of a sequence of words. Results on various classification and sequence labelling benchmarks show that the proposed model has strong representation power, giving highly competitive performances compared to stacked BiLSTM models with similar parameter numbers.

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Chinese NER Using Lattice LSTM
Yue Zhang | Jie Yang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with character-based methods, our model explicitly leverages word and word sequence information. Compared with word-based methods, lattice LSTM does not suffer from segmentation errors. Gated recurrent cells allow our model to choose the most relevant characters and words from a sentence for better NER results. Experiments on various datasets show that lattice LSTM outperforms both word-based and character-based LSTM baselines, achieving the best results.

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A Graph-to-Sequence Model for AMR-to-Text Generation
Linfeng Song | Yue Zhang | Zhiguo Wang | Daniel Gildea
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although being able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus facing challenges with large-graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature.

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A Rank-Based Similarity Metric for Word Embeddings
Enrico Santus | Hongmin Wang | Emmanuele Chersoni | Yue Zhang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Word Embeddings have recently imposed themselves as a standard for representing word meaning in NLP. Semantic similarity between word pairs has become the most common evaluation benchmark for these representations, with vector cosine being typically used as the only similarity metric. In this paper, we report experiments with a rank-based metric for WE, which performs comparably to vector cosine in similarity estimation and outperforms it in the recently-introduced and challenging task of outlier detection, thus suggesting that rank-based measures can improve clustering quality.

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YEDDA: A Lightweight Collaborative Text Span Annotation Tool
Jie Yang | Yue Zhang | Linwei Li | Xingxuan Li
Proceedings of ACL 2018, System Demonstrations

In this paper, we introduce Yedda, a lightweight but efficient and comprehensive open-source tool for text span annotation. Yedda provides a systematic solution for text span annotation, ranging from collaborative user annotation to administrator evaluation and analysis. It overcomes the low efficiency of traditional text annotation tools by annotating entities through both command line and shortcut keys, which are configurable with custom labels. Yedda also gives intelligent recommendations by learning the up-to-date annotated text. An administrator client is developed to evaluate annotation quality of multiple annotators and generate detailed comparison report for each annotator pair. Experiments show that the proposed system can reduce the annotation time by half compared with existing annotation tools. And the annotation time can be further compressed by 16.47% through intelligent recommendation.

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NCRF++: An Open-source Neural Sequence Labeling Toolkit
Jie Yang | Yue Zhang
Proceedings of ACL 2018, System Demonstrations

This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer. It provides users with an inference for building the custom model structure through configuration file with flexible neural feature design and utilization. Built on PyTorch http://pytorch.org/, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. It also includes the implementations of most state-of-the-art neural sequence labeling models such as LSTM-CRF, facilitating reproducing and refinement on those methods.

2017

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Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing
Yue Zhang | Zhifang Sui
Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing

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Encoder-Decoder Shift-Reduce Syntactic Parsing
Jiangming Liu | Yue Zhang
Proceedings of the 15th International Conference on Parsing Technologies

Encoder-decoder neural networks have been used for many NLP tasks, such as neural machine translation. They have also been applied to constituent parsing by using bracketed tree structures as a target language, translating input sentences into syntactic trees. A more commonly used method to linearize syntactic trees is the shift-reduce system, which uses a sequence of transition-actions to build trees. We empirically investigate the effectiveness of applying the encoder-decoder network to transition-based parsing. On standard benchmarks, our system gives comparable results to the stack LSTM parser for dependency parsing, and significantly better results compared to the aforementioned parser for constituent parsing, which uses bracketed tree formats.

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Neural Word Segmentation with Rich Pretraining
Jie Yang | Yue Zhang | Fei Dong
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.

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Universal Dependencies Parsing for Colloquial Singaporean English
Hongmin Wang | Yue Zhang | GuangYong Leonard Chan | Jie Yang | Hai Leong Chieu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.

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AMR-to-text Generation with Synchronous Node Replacement Grammar
Linfeng Song | Xiaochang Peng | Yue Zhang | Zhiguo Wang | Daniel Gildea
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on a standard benchmark, our method gives the state-of-the-art result.

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Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring
Fei Dong | Yue Zhang | Jie Yang
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Neural network models have recently been applied to the task of automatic essay scoring, giving promising results. Existing work used recurrent neural networks and convolutional neural networks to model input essays, giving grades based on a single vector representation of the essay. On the other hand, the relative advantages of RNNs and CNNs have not been compared. In addition, different parts of the essay can contribute differently for scoring, which is not captured by existing models. We address these issues by building a hierarchical sentence-document model to represent essays, using the attention mechanism to automatically decide the relative weights of words and sentences. Results show that our model outperforms the previous state-of-the-art methods, demonstrating the effectiveness of the attention mechanism.

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Neural Reranking for Named Entity Recognition
Jie Yang | Yue Zhang | Fei Dong
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

We propose a neural reranking system for named entity recognition (NER), leverages recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as Barack Obama, into their entity types, such as PER. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, “PER was born in LOC” can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.

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Integrating Order Information and Event Relation for Script Event Prediction
Zhongqing Wang | Yue Zhang | Ching-Yun Chang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

There has been a recent line of work automatically learning scripts from unstructured texts, by modeling narrative event chains. While the dominant approach group events using event pair relations, LSTMs have been used to encode full chains of narrative events. The latter has the advantage of learning long-range temporal orders, yet the former is more adaptive to partial orders. We propose a neural model that leverages the advantages of both methods, by using LSTM hidden states as features for event pair modelling. A dynamic memory network is utilized to automatically induce weights on existing events for inferring a subsequent event. Standard evaluation shows that our method significantly outperforms both methods above, giving the best results reported so far.

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Word-Context Character Embeddings for Chinese Word Segmentation
Hao Zhou | Zhenting Yu | Yue Zhang | Shujian Huang | Xinyu Dai | Jiajun Chen
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Neural parsers have benefited from automatically labeled data via dependency-context word embeddings. We investigate training character embeddings on a word-based context in a similar way, showing that the simple method improves state-of-the-art neural word segmentation models significantly, beating tri-training baselines for leveraging auto-segmented data.

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Opinion Recommendation Using A Neural Model
Zhongqing Wang | Yue Zhang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present opinion recommendation, a novel task of jointly generating a review with a rating score that a certain user would give to a certain product which is unreviewed by the user, given existing reviews to the product by other users, and the reviews that the user has given to other products. A characteristic of opinion recommendation is the reliance of multiple data sources for multi-task joint learning. We use a single neural network to model users and products, generating customised product representations using a deep memory network, from which customised ratings and reviews are constructed jointly. Results show that our opinion recommendation system gives ratings that are closer to real user ratings on Yelp.com data compared with Yelp’s own ratings. our methods give better results compared to several pipelines baselines.

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End-to-End Neural Relation Extraction with Global Optimization
Meishan Zhang | Yue Zhang | Guohong Fu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Neural networks have shown promising results for relation extraction. State-of-the-art models cast the task as an end-to-end problem, solved incrementally using a local classifier. Yet previous work using statistical models have demonstrated that global optimization can achieve better performances compared to local classification. We build a globally optimized neural model for end-to-end relation extraction, proposing novel LSTM features in order to better learn context representations. In addition, we present a novel method to integrate syntactic information to facilitate global learning, yet requiring little background on syntactic grammars thus being easy to extend. Experimental results show that our proposed model is highly effective, achieving the best performances on two standard benchmarks.

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Transition-Based Disfluency Detection using LSTMs
Shaolei Wang | Wanxiang Che | Yue Zhang | Meishan Zhang | Ting Liu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

In this paper, we model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a new transition system without syntax information. Compared with sequence labeling methods, it can capture non-local chunk-level features; compared with joint parsing and disfluency detection methods, it is free for noise in syntax. Experiments show that our model achieves state-of-the-art f-score of 87.5% on the commonly used English Switchboard test set, and a set of in-house annotated Chinese data.

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Transition-Based Deep Input Linearization
Ratish Puduppully | Yue Zhang | Manish Shrivastava
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Traditional methods for deep NLG adopt pipeline approaches comprising stages such as constructing syntactic input, predicting function words, linearizing the syntactic input and generating the surface forms. Though easier to visualize, pipeline approaches suffer from error propagation. In addition, information available across modules cannot be leveraged by all modules. We construct a transition-based model to jointly perform linearization, function word prediction and morphological generation, which considerably improves upon the accuracy compared to a pipelined baseline system. On a standard deep input linearization shared task, our system achieves the best results reported so far.

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Attention Modeling for Targeted Sentiment
Jiangming Liu | Yue Zhang
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Neural network models have been used for target-dependent sentiment analysis. Previous work focus on learning a target specific representation for a given input sentence which is used for classification. However, they do not explicitly model the contribution of each word in a sentence with respect to targeted sentiment polarities. We investigate an attention model to this end. In particular, a vanilla LSTM model is used to induce an attention value of the whole sentence. The model is further extended to differentiate left and right contexts given a certain target following previous work. Results show that by using attention to model the contribution of each word with respect to the target, our model gives significantly improved results over two standard benchmarks. We report the best accuracy for this task.

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Dependency Parsing with Partial Annotations: An Empirical Comparison
Yue Zhang | Zhenghua Li | Jun Lang | Qingrong Xia | Min Zhang
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This paper describes and compares two straightforward approaches for dependency parsing with partial annotations (PA). The first approach is based on a forest-based training objective for two CRF parsers, i.e., a biaffine neural network graph-based parser (Biaffine) and a traditional log-linear graph-based parser (LLGPar). The second approach is based on the idea of constrained decoding for three parsers, i.e., a traditional linear graph-based parser (LGPar), a globally normalized neural network transition-based parser (GN3Par) and a traditional linear transition-based parser (LTPar). For the test phase, constrained decoding is also used for completing partial trees. We conduct experiments on Penn Treebank under three different settings for simulating PA, i.e., random, most uncertain, and divergent outputs from the five parsers. The results show that LLGPar is most effective in directly learning from PA, and other parsers can achieve best performance when PAs are completed into full trees by LLGPar.

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Implicit Syntactic Features for Target-dependent Sentiment Analysis
Yuze Gao | Yue Zhang | Tong Xiao
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Targeted sentiment analysis investigates the sentiment polarities on given target mentions from input texts. Different from sentence level sentiment, it offers more fine-grained knowledge on each entity mention. While early work leveraged syntactic information, recent research has used neural representation learning to induce features automatically, thereby avoiding error propagation of syntactic parsers, which are particularly severe on social media texts. We study a method to leverage syntactic information without explicitly building the parser outputs, by training an encoder-decoder structure parser model on standard syntactic treebanks, and then leveraging its hidden encoder layers when analysing tweets. Such hidden vectors do not contain explicit syntactic outputs, yet encode rich syntactic features. We use them to augment the inputs to a baseline state-of-the-art targeted sentiment classifier, observing significant improvements on various benchmark datasets. We obtain the best accuracies on all test sets.

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Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization
Litton J Kurisinkel | Yue Zhang | Vasudeva Varma
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Existing work for abstractive multidocument summarization utilise existing phrase structures directly extracted from input documents to generate summary sentences. These methods can suffer from lack of consistence and coherence in merging phrases. We introduce a novel approach for abstractive multidocument summarization through partial dependency tree extraction, recombination and linearization. The method entrusts the summarizer to generate its own topically coherent sequential structures from scratch for effective communication. Results on TAC 2011, DUC-2004 and 2005 show that our system gives competitive results compared with state of the art abstractive summarization approaches in the literature. We also achieve competitive results in linguistic quality assessed by human evaluators.

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Deep Learning in Lexical Analysis and Parsing
Wanxiang Che | Yue Zhang
Proceedings of the IJCNLP 2017, Tutorial Abstracts

Neural networks, also with a fancy name deep learning, just right can overcome the above “feature engineering” problem. In theory, they can use non-linear activation functions and multiple layers to automatically find useful features. The novel network structures, such as convolutional or recurrent, help to reduce the difficulty further. These deep learning models have been successfully used for lexical analysis and parsing. In this tutorial, we will give a review of each line of work, by contrasting them with traditional statistical methods, and organizing them in consistent orders.

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Shift-Reduce Constituent Parsing with Neural Lookahead Features
Jiangming Liu | Yue Zhang
Transactions of the Association for Computational Linguistics, Volume 5

Transition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a parser stack, which consists of a sequence of non-local constituents. On the other hand, during incremental parsing, constituent information on the right hand side of the current word is not utilized, which is a relative weakness of shift-reduce parsing. To address this limitation, we leverage a fast neural model to extract lookahead features. In particular, we build a bidirectional LSTM model, which leverages full sentence information to predict the hierarchy of constituents that each word starts and ends. The results are then passed to a strong transition-based constituent parser as lookahead features. The resulting parser gives 1.3% absolute improvement in WSJ and 2.3% in CTB compared to the baseline, giving the highest reported accuracies for fully-supervised parsing.

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Head-Lexicalized Bidirectional Tree LSTMs
Zhiyang Teng | Yue Zhang
Transactions of the Association for Computational Linguistics, Volume 5

Sequential LSTMs have been extended to model tree structures, giving competitive results for a number of tasks. Existing methods model constituent trees by bottom-up combinations of constituent nodes, making direct use of input word information only for leaf nodes. This is different from sequential LSTMs, which contain references to input words for each node. In this paper, we propose a method for automatic head-lexicalization for tree-structure LSTMs, propagating head words from leaf nodes to every constituent node. In addition, enabled by head lexicalization, we build a tree LSTM in the top-down direction, which corresponds to bidirectional sequential LSTMs in structure. Experiments show that both extensions give better representations of tree structures. Our final model gives the best results on the Stanford Sentiment Treebank and highly competitive results on the TREC question type classification task.

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In-Order Transition-based Constituent Parsing
Jiangming Liu | Yue Zhang
Transactions of the Association for Computational Linguistics, Volume 5

Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation tree, where bottom-up strategies and top-down strategies take post-order and pre-order traversal over trees, respectively. Bottom-up parsers benefit from rich features from readily built partial parses, but lack lookahead guidance in the parsing process; top-down parsers benefit from non-local guidance for local decisions, but rely on a strong encoder over the input to predict a constituent hierarchy before its construction. To mitigate both issues, we propose a novel parsing system based on in-order traversal over syntactic trees, designing a set of transition actions to find a compromise between bottom-up constituent information and top-down lookahead information. Based on stack-LSTM, our psycholinguistically motivated constituent parsing system achieves 91.8 F1 on the WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised reranking and 94.2 F1 with semi-supervised reranking, which are the best results on the WSJ benchmark.

2016

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LibN3L:A Lightweight Package for Neural NLP
Meishan Zhang | Jie Yang | Zhiyang Teng | Yue Zhang
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present a light-weight machine learning tool for NLP research. The package supports operations on both discrete and dense vectors, facilitating implementation of linear models as well as neural models. It provides several basic layers which mainly aims for single-layer linear and non-linear transformations. By using these layers, we can conveniently implement linear models and simple neural models. Besides, this package also integrates several complex layers by composing those basic layers, such as RNN, Attention Pooling, LSTM and gated RNN. Those complex layers can be used to implement deep neural models directly.

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Evaluating a Deterministic Shift-Reduce Neural Parser for Constituent Parsing
Hao Zhou | Yue Zhang | Shujian Huang | Xin-Yu Dai | Jiajun Chen
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Greedy transition-based parsers are appealing for their very fast speed, with reasonably high accuracies. In this paper, we build a fast shift-reduce neural constituent parser by using a neural network to make local decisions. One challenge to the parsing speed is the large hidden and output layer sizes caused by the number of constituent labels and branching options. We speed up the parser by using a hierarchical output layer, inspired by the hierarchical log-bilinear neural language model. In standard WSJ experiments, the neural parser achieves an almost 2.4 time speed up (320 sen/sec) compared to a non-hierarchical baseline without significant accuracy loss (89.06 vs 89.13 F-score).

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Multi-prototype Chinese Character Embedding
Yanan Lu | Yue Zhang | Donghong Ji
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Chinese sentences are written as sequences of characters, which are elementary units of syntax and semantics. Characters are highly polysemous in forming words. We present a position-sensitive skip-gram model to learn multi-prototype Chinese character embeddings, and explore the usefulness of such character embeddings to Chinese NLP tasks. Evaluation on character similarity shows that multi-prototype embeddings are significantly better than a single-prototype baseline. In addition, used as features in the Chinese NER task, the embeddings result in a 1.74% F-score improvement over a state-of-the-art baseline.

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Assessing the Prosody of Non-Native Speakers of English: Measures and Feature Sets
Eduardo Coutinho | Florian Hönig | Yue Zhang | Simone Hantke | Anton Batliner | Elmar Nöth | Björn Schuller
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, we describe a new database with audio recordings of non-native (L2) speakers of English, and the perceptual evaluation experiment conducted with native English speakers for assessing the prosody of each recording. These annotations are then used to compute the gold standard using different methods, and a series of regression experiments is conducted to evaluate their impact on the performance of a regression model predicting the degree of naturalness of L2 speech. Further, we compare the relevance of different feature groups modelling prosody in general (without speech tempo), speech rate and pauses modelling speech tempo (fluency), voice quality, and a variety of spectral features. We also discuss the impact of various fusion strategies on performance. Overall, our results demonstrate that the prosody of non-native speakers of English as L2 can be reliably assessed using supra-segmental audio features; prosodic features seem to be the most important ones.

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Expectation-Regulated Neural Model for Event Mention Extraction
Ching-Yun Chang | Zhiyang Teng | Yue Zhang
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Transition-Based Syntactic Linearization with Lookahead Features
Ratish Puduppully | Yue Zhang | Manish Shrivastava
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Active Learning for Dependency Parsing with Partial Annotation
Zhenghua Li | Min Zhang | Yue Zhang | Zhanyi Liu | Wenliang Chen | Hua Wu | Haifeng Wang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Transition-Based Neural Word Segmentation
Meishan Zhang | Yue Zhang | Guohong Fu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Search-Based Dynamic Reranking Model for Dependency Parsing
Hao Zhou | Yue Zhang | Shujian Huang | Junsheng Zhou | Xin-Yu Dai | Jiajun Chen
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Don’t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text
Duy Tin Vo | Yue Zhang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Neural Network for Heterogeneous Annotations
Hongshen Chen | Yue Zhang | Qun Liu
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Exploiting Mutual Benefits between Syntax and Semantic Roles using Neural Network
Peng Shi | Zhiyang Teng | Yue Zhang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Automatic Features for Essay Scoring – An Empirical Study
Fei Dong | Yue Zhang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Context-Sensitive Lexicon Features for Neural Sentiment Analysis
Zhiyang Teng | Duy-Tin Vo | Yue Zhang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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AMR-to-text generation as a Traveling Salesman Problem
Linfeng Song | Yue Zhang | Xiaochang Peng | Zhiguo Wang | Daniel Gildea
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Neural Networks for Sentiment Analysis
Yue Zhang | Duy Tin Vo
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Sentiment analysis has been a major research topic in natural language processing (NLP). Traditionally, the problem has been attacked using discrete models and manually-defined sparse features. Over the past few years, neural network models have received increased research efforts in most sub areas of sentiment analysis, giving highly promising results. A main reason is the capability of neural models to automatically learn dense features that capture subtle semantic information over words, sentences and documents, which are difficult to model using traditional discrete features based on words and ngram patterns. This tutorial gives an introduction to neural network models for sentiment analysis, discussing the mathematics of word embeddings, sequence models and tree structured models and their use in sentiment analysis on the word, sentence and document levels, and fine-grained sentiment analysis. The tutorial covers a range of neural network models (e.g. CNN, RNN, RecNN, LSTM) and their extensions, which are employed in four main subtasks of sentiment analysis:Sentiment-oriented embeddings;Sentence-level sentiment;Document-level sentiment;Fine-grained sentiment.The content of the tutorial is divided into 3 sections of 1 hour each. We assume that the audience is familiar with linear algebra and basic neural network structures, introduce the mathematical details of the most typical models. First, we will introduce the sentiment analysis task, basic concepts related to neural network models for sentiment analysis, and show detail approaches to integrate sentiment information into embeddings. Sentence-level models will be described in the second section. Finally, we will discuss neural network models use for document-level and fine-grained sentiment.

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Deceptive Opinion Spam Detection Using Neural Network
Yafeng Ren | Yue Zhang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Deceptive opinion spam detection has attracted significant attention from both business and research communities. Existing approaches are based on manual discrete features, which can capture linguistic and psychological cues. However, such features fail to encode the semantic meaning of a document from the discourse perspective, which limits the performance. In this paper, we empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam. In particular, given a document, the model learns sentence representations with a convolutional neural network, which are combined using a gated recurrent neural network with attention mechanism to model discourse information and yield a document vector. Finally, the document representation is used directly as features to identify deceptive opinion spam. Experimental results on three domains (Hotel, Restaurant, and Doctor) show that our proposed method outperforms state-of-the-art methods.

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A Bilingual Attention Network for Code-switched Emotion Prediction
Zhongqing Wang | Yue Zhang | Sophia Lee | Shoushan Li | Guodong Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Emotions in code-switching text can be expressed in either monolingual or bilingual forms. However, relatively little research has emphasized on code-switching text. In this paper, we propose a Bilingual Attention Network (BAN) model to aggregate the monolingual and bilingual informative words to form vectors from the document representation, and integrate the attention vectors to predict the emotion. The experiments show that the effectiveness of the proposed model. Visualization of the attention layers illustrates that the model selects qualitatively informative words.

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Knowledge-Driven Event Embedding for Stock Prediction
Xiao Ding | Yue Zhang | Ting Liu | Junwen Duan
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications. Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as event-driven stock prediction. On the other hand, events extracted from raw texts do not contain background knowledge on entities and relations that they are mentioned. To address this issue, this paper proposes to leverage extra information from knowledge graph, which provides ground truth such as attributes and properties of entities and encodes valuable relations between entities. Specifically, we propose a joint model to combine knowledge graph information into the objective function of an event embedding learning model. Experiments on event similarity and stock market prediction show that our model is more capable of obtaining better event embeddings and making more accurate prediction on stock market volatilities.

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Tweet Sarcasm Detection Using Deep Neural Network
Meishan Zhang | Yue Zhang | Guohong Fu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features.

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Distance Metric Learning for Aspect Phrase Grouping
Shufeng Xiong | Yue Zhang | Donghong Ji | Yinxia Lou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Aspect phrase grouping is an important task in aspect-level sentiment analysis. It is a challenging problem due to polysemy and context dependency. We propose an Attention-based Deep Distance Metric Learning (ADDML) method, by considering aspect phrase representation as well as context representation. First, leveraging the characteristics of the review text, we automatically generate aspect phrase sample pairs for distant supervision. Second, we feed word embeddings of aspect phrases and their contexts into an attention-based neural network to learn feature representation of contexts. Both aspect phrase embedding and context embedding are used to learn a deep feature subspace for measure the distances between aspect phrases for K-means clustering. Experiments on four review datasets show that the proposed method outperforms state-of-the-art strong baseline methods.

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Measuring the Information Content of Financial News
Ching-Yun Chang | Yue Zhang | Zhiyang Teng | Zahn Bozanic | Bin Ke
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Measuring the information content of news text is useful for decision makers in their investments since news information can influence the intrinsic values of companies. We propose a model to automatically measure the information content given news text, trained using news and corresponding cumulative abnormal returns of listed companies. Existing methods in finance literature exploit sentiment signal features, which are limited by not considering factors such as events. We address this issue by leveraging deep neural models to extract rich semantic features from news text. In particular, a novel tree-structured LSTM is used to find target-specific representations of news text given syntax structures. Empirical results show that the neural models can outperform sentiment-based models, demonstrating the effectiveness of recent NLP technology advances for computational finance.

2015

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An Empirical Comparison Between N-gram and Syntactic Language Models for Word Ordering
Jiangming Liu | Yue Zhang
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Neural Networks for Open Domain Targeted Sentiment
Meishan Zhang | Yue Zhang | Duy-Tin Vo
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Combining Discrete and Continuous Features for Deterministic Transition-based Dependency Parsing
Meishan Zhang | Yue Zhang
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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A Transition-based Model for Joint Segmentation, POS-tagging and Normalization
Tao Qian | Yue Zhang | Meishan Zhang | Yafeng Ren | Donghong Ji
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Syntactic Dependencies and Distributed Word Representations for Analogy Detection and Mining
Likun Qiu | Yue Zhang | Yanan Lu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Event-Driven Headline Generation
Rui Sun | Yue Zhang | Meishan Zhang | Donghong Ji
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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A Neural Probabilistic Structured-Prediction Model for Transition-Based Dependency Parsing
Hao Zhou | Yue Zhang | Shujian Huang | Jiajun Chen
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Transition-Based Syntactic Linearization
Yijia Liu | Yue Zhang | Wanxiang Che | Bing Qin
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing
Liang-Chih Yu | Zhifang Sui | Yue Zhang | Vincent Ng
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing

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Discriminative Syntax-Based Word Ordering for Text Generation
Yue Zhang | Stephen Clark
Computational Linguistics, Volume 41, Issue 3 - September 2015

2014

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Type-Supervised Domain Adaptation for Joint Segmentation and POS-Tagging
Meishan Zhang | Yue Zhang | Wanxiang Che | Ting Liu
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Tagging The Web: Building A Robust Web Tagger with Neural Network
Ji Ma | Yue Zhang | Jingbo Zhu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Shift-Reduce CCG Parsing with a Dependency Model
Wenduan Xu | Stephen Clark | Yue Zhang
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Character-Level Chinese Dependency Parsing
Meishan Zhang | Yue Zhang | Wanxiang Che | Ting Liu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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On WordNet Semantic Classes and Dependency Parsing
Kepa Bengoetxea | Eneko Agirre | Joakim Nivre | Yue Zhang | Koldo Gojenola
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Punctuation Processing for Projective Dependency Parsing
Ji Ma | Yue Zhang | Jingbo Zhu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Syntactic Processing Using Global Discriminative Learning and Beam-Search Decoding
Yue Zhang | Meishan Zhang | Ting Liu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Tutorials

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Multi-view Chinese Treebanking
Likun Qiu | Yue Zhang | Peng Jin | Houfeng Wang
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Feature Embedding for Dependency Parsing
Wenliang Chen | Yue Zhang | Min Zhang
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Syntactic SMT Using a Discriminative Text Generation Model
Yue Zhang | Kai Song | Linfeng Song | Jingbo Zhu | Qun Liu
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Domain Adaptation for CRF-based Chinese Word Segmentation using Free Annotations
Yijia Liu | Yue Zhang | Wanxiang Che | Ting Liu | Fan Wu
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Using Structured Events to Predict Stock Price Movement: An Empirical Investigation
Xiao Ding | Yue Zhang | Ting Liu | Junwen Duan
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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ZORE: A Syntax-based System for Chinese Open Relation Extraction
Likun Qiu | Yue Zhang
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Semi-Supervised Feature Transformation for Dependency Parsing
Wenliang Chen | Min Zhang | Yue Zhang
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Feature-Rich Segment-Based News Event Detection on Twitter
Yanxia Qin | Yue Zhang | Min Zhang | Dequan Zheng
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Chinese Parsing Exploiting Characters
Meishan Zhang | Yue Zhang | Wanxiang Che | Ting Liu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Fast and Accurate Shift-Reduce Constituent Parsing
Muhua Zhu | Yue Zhang | Wenliang Chen | Min Zhang | Jingbo Zhu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning to Prune: Context-Sensitive Pruning for Syntactic MT
Wenduan Xu | Yue Zhang | Philip Williams | Philipp Koehn
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Syntax-Based Word Ordering Incorporating a Large-Scale Language Model
Yue Zhang | Graeme Blackwood | Stephen Clark
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Unsupervised Domain Adaptation for Joint Segmentation and POS-Tagging
Yang Liu | Yue Zhang
Proceedings of COLING 2012: Posters

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Analyzing the Effect of Global Learning and Beam-Search on Transition-Based Dependency Parsing
Yue Zhang | Joakim Nivre
Proceedings of COLING 2012: Posters

2011

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Shift-Reduce CCG Parsing
Yue Zhang | Stephen Clark
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Transition-based Dependency Parsing with Rich Non-local Features
Yue Zhang | Joakim Nivre
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Syntactic Processing Using the Generalized Perceptron and Beam Search
Yue Zhang | Stephen Clark
Computational Linguistics, Volume 37, Issue 1 - March 2011

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Syntax-Based Grammaticality Improvement using CCG and Guided Search
Yue Zhang | Stephen Clark
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Recent Advances in Dependency Parsing
Qin Iris Wang | Yue Zhang
NAACL HLT 2010 Tutorial Abstracts

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A Fast Decoder for Joint Word Segmentation and POS-Tagging Using a Single Discriminative Model
Yue Zhang | Stephen Clark
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Chart Pruning for Fast Lexicalised-Grammar Parsing
Yue Zhang | Byung-Gyu Ahn | Stephen Clark | Curt Van Wyk | James R. Curran | Laura Rimell
Coling 2010: Posters

2009

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Transition-Based Parsing of the Chinese Treebank using a Global Discriminative Model
Yue Zhang | Stephen Clark
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)

2008

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A Tale of Two Parsers: Investigating and Combining Graph-based and Transition-based Dependency Parsing
Yue Zhang | Stephen Clark
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Joint Word Segmentation and POS Tagging Using a Single Perceptron
Yue Zhang | Stephen Clark
Proceedings of ACL-08: HLT

2007

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Chinese Segmentation with a Word-Based Perceptron Algorithm
Yue Zhang | Stephen Clark
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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