Ming Zhou


2021

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InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training
Zewen Chi | Li Dong | Furu Wei | Nan Yang | Saksham Singhal | Wenhui Wang | Xia Song | Xian-Ling Mao | Heyan Huang | Ming Zhou
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In this work, we present an information-theoretic framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts. The unified view helps us to better understand the existing methods for learning cross-lingual representations. More importantly, inspired by the framework, we propose a new pre-training task based on contrastive learning. Specifically, we regard a bilingual sentence pair as two views of the same meaning and encourage their encoded representations to be more similar than the negative examples. By leveraging both monolingual and parallel corpora, we jointly train the pretext tasks to improve the cross-lingual transferability of pre-trained models. Experimental results on several benchmarks show that our approach achieves considerably better performance. The code and pre-trained models are available at https://aka.ms/infoxlm.

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Smart-Start Decoding for Neural Machine Translation
Jian Yang | Shuming Ma | Dongdong Zhang | Juncheng Wan | Zhoujun Li | Ming Zhou
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Most current neural machine translation models adopt a monotonic decoding order of either left-to-right or right-to-left. In this work, we propose a novel method that breaks up the limitation of these decoding orders, called Smart-Start decoding. More specifically, our method first predicts a median word. It starts to decode the words on the right side of the median word and then generates words on the left. We evaluate the proposed Smart-Start decoding method on three datasets. Experimental results show that the proposed method can significantly outperform strong baseline models.

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Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge
Linmei Hu | Tianchi Yang | Luhao Zhang | Wanjun Zhong | Duyu Tang | Chuan Shi | Nan Duan | Ming Zhou
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)

Nowadays, fake news detection, which aims to verify whether a news document is trusted or fake, has become urgent and important. Most existing methods rely heavily on linguistic and semantic features from the news content, and fail to effectively exploit external knowledge which could help determine whether the news document is trusted. In this paper, we propose a novel end-to-end graph neural model called CompareNet, which compares the news to the knowledge base (KB) through entities for fake news detection. Considering that fake news detection is correlated with topics, we also incorporate topics to enrich the news representation. Specifically, we first construct a directed heterogeneous document graph for each news incorporating topics and entities. Based on the graph, we develop a heterogeneous graph attention network for learning the topic-enriched news representation as well as the contextual entity representations that encode the semantics of the news content. The contextual entity representations are then compared to the corresponding KB-based entity representations through a carefully designed entity comparison network, to capture the consistency between the news content and KB. Finally, the topic-enriched news representation combining the entity comparison features is fed into a fake news classifier. Experimental results on two benchmark datasets demonstrate that CompareNet significantly outperforms state-of-the-art methods.

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Control Image Captioning Spatially and Temporally
Kun Yan | Lei Ji | Huaishao Luo | Ming Zhou | Nan Duan | Shuai Ma
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)

Generating image captions with user intention is an emerging need. The recently published Localized Narratives dataset takes mouse traces as another input to the image captioning task, which is an intuitive and efficient way for a user to control what to describe in the image. However, how to effectively employ traces to improve generation quality and controllability is still under exploration. This paper aims to solve this problem by proposing a novel model called LoopCAG, which connects Contrastive constraints and Attention Guidance in a Loop manner, engaged explicit spatial and temporal constraints to the generating process. Precisely, each generated sentence is temporally aligned to the corresponding trace sequence through a contrastive learning strategy. Besides, each generated text token is supervised to attend to the correct visual objects under heuristic spatial attention guidance. Comprehensive experimental results demonstrate that our LoopCAG model learns better correspondence among the three modalities (vision, language, and traces) and achieves SOTA performance on trace-controlled image captioning task. Moreover, the controllability and explainability of LoopCAG are validated by analyzing spatial and temporal sensitivity during the generation process.

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SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation
Shuo Ren | Long Zhou | Shujie Liu | Furu Wei | Ming Zhou | Shuai Ma
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 pre-training techniques are working very well in natural language processing, how to pre-train a decoder and effectively use it for neural machine translation (NMT) still remains a tricky issue. The main reason is that the cross-attention module between the encoder and decoder cannot be pre-trained, and the combined encoder-decoder model cannot work well in the fine-tuning stage because the inputs of the decoder cross-attention come from unknown encoder outputs. In this paper, we propose a better pre-training method for NMT by defining a semantic interface (SemFace) between the pre-trained encoder and the pre-trained decoder. Specifically, we propose two types of semantic interfaces, including CL-SemFace which regards cross-lingual embeddings as an interface, and VQ-SemFace which employs vector quantized embeddings to constrain the encoder outputs and decoder inputs in the same language-independent space. We conduct massive experiments on six supervised translation pairs and three unsupervised pairs. Experimental results demonstrate that our proposed SemFace can effectively connect the pre-trained encoder and decoder, and achieves significant improvement by 3.7 and 1.5 BLEU points on the two tasks respectively compared with previous pre-training-based NMT models.

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Learning to Ask Conversational Questions by Optimizing Levenshtein Distance
Zhongkun Liu | Pengjie Ren | Zhumin Chen | Zhaochun Ren | Maarten de Rijke | Ming Zhou
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)

Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e.g., anaphora and ellipsis. Existing maximum likelihood estimation based methods often get trapped in easily learned tokens as all tokens are treated equally during training. In this work, we introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimizes the minimum Levenshtein distance through explicit editing actions. RISE is able to pay attention to tokens that are related to conversational characteristics. To train RISE, we devise an Iterative Reinforce Training (IRT) algorithm with a Dynamic Programming based Sampling (DPS) process to improve exploration. Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods and generalizes well on unseen data.

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CoSQA: 20,000+ Web Queries for Code Search and Question Answering
Junjie Huang | Duyu Tang | Linjun Shou | Ming Gong | Ke Xu | Daxin Jiang | Ming Zhou | Nan Duan
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)

Finding codes given natural language query is beneficial to the productivity of software developers. Future progress towards better semantic matching between query and code requires richer supervised training resources. To remedy this, we introduce CoSQA dataset. It includes 20,604 labels for pairs of natural language queries and codes, each annotated by at least 3 human annotators. We further introduce a contrastive learning method dubbed CoCLR to enhance text-code matching, which works as a data augmenter to bring more artificially generated training instances. We show that, evaluated on CodeXGLUE with the same CodeBERT model, training on CoSQA improves the accuracy of code question answering by 5.1% and incorporating CoCLR brings a further improvement of 10.5%.

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GLGE: A New General Language Generation Evaluation Benchmark
Dayiheng Liu | Yu Yan | Yeyun Gong | Weizhen Qi | Hang Zhang | Jian Jiao | Weizhu Chen | Jie Fu | Linjun Shou | Ming Gong | Pengcheng Wang | Jiusheng Chen | Daxin Jiang | Jiancheng Lv | Ruofei Zhang | Winnie Wu | Ming Zhou | Nan Duan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Grammar-Based Patches Generation for Automated Program Repair
Yu Tang | Long Zhou | Ambrosio Blanco | Shujie Liu | Furu Wei | Ming Zhou | Muyun Yang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Ruize Wang | Duyu Tang | Nan Duan | Zhongyu Wei | Xuanjing Huang | Jianshu Ji | Guihong Cao | Daxin Jiang | Ming Zhou
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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DocBank: A Benchmark Dataset for Document Layout Analysis
Minghao Li | Yiheng Xu | Lei Cui | Shaohan Huang | Furu Wei | Zhoujun Li | Ming Zhou
Proceedings of the 28th International Conference on Computational Linguistics

Document layout analysis usually relies on computer vision models to understand documents while ignoring textual information that is vital to capture. Meanwhile, high quality labeled datasets with both visual and textual information are still insufficient. In this paper, we present DocBank, a benchmark dataset that contains 500K document pages with fine-grained token-level annotations for document layout analysis. DocBank is constructed using a simple yet effective way with weak supervision from the LaTeX documents available on the arXiv.com. With DocBank, models from different modalities can be compared fairly and multi-modal approaches will be further investigated and boost the performance of document layout analysis. We build several strong baselines and manually split train/dev/test sets for evaluation. Experiment results show that models trained on DocBank accurately recognize the layout information for a variety of documents. The DocBank dataset is publicly available at https://github.com/doc-analysis/DocBank.

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Unsupervised Fine-tuning for Text Clustering
Shaohan Huang | Furu Wei | Lei Cui | Xingxing Zhang | Ming Zhou
Proceedings of the 28th International Conference on Computational Linguistics

Fine-tuning with pre-trained language models (e.g. BERT) has achieved great success in many language understanding tasks in supervised settings (e.g. text classification). However, relatively little work has been focused on applying pre-trained models in unsupervised settings, such as text clustering. In this paper, we propose a novel method to fine-tune pre-trained models unsupervisedly for text clustering, which simultaneously learns text representations and cluster assignments using a clustering oriented loss. Experiments on three text clustering datasets (namely TREC-6, Yelp, and DBpedia) show that our model outperforms the baseline methods and achieves state-of-the-art results.

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At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization
Qingyu Zhou | Furu Wei | Ming Zhou
Proceedings of the 28th International Conference on Computational Linguistics

Extractive methods have been proven effective in automatic document summarization. Previous works perform this task by identifying informative contents at sentence level. However, it is unclear whether performing extraction at sentence level is the best solution. In this work, we show that unnecessity and redundancy issues exist when extracting full sentences, and extracting sub-sentential units is a promising alternative. Specifically, we propose extracting sub-sentential units based on the constituency parsing tree. A neural extractive model which leverages the sub-sentential information and extracts them is presented. Extensive experiments and analyses show that extracting sub-sentential units performs competitively comparing to full sentence extraction under the evaluation of both automatic and human evaluations. Hopefully, our work could provide some inspiration of the basic extraction units in extractive summarization for future research.

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TableBank: Table Benchmark for Image-based Table Detection and Recognition
Minghao Li | Lei Cui | Shaohan Huang | Furu Wei | Ming Zhou | Zhoujun Li
Proceedings of the 12th Language Resources and Evaluation Conference

We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousand human-labeled examples, which is difficult to generalize on real-world applications. With TableBank that contains 417K high quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks. We make TableBank publicly available and hope it will empower more deep learning approaches in the table detection and recognition task. The dataset and models can be downloaded from https://github.com/doc-analysis/TableBank.

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Improving Grammatical Error Correction with Machine Translation Pairs
Wangchunshu Zhou | Tao Ge | Chang Mu | Ke Xu | Furu Wei | Ming Zhou
Findings of the Association for Computational Linguistics: EMNLP 2020

We propose a novel data synthesis method to generate diverse error-corrected sentence pairs for improving grammatical error correction, which is based on a pair of machine translation models (e.g., Chinese to English) of different qualities (i.e., poor and good). The poor translation model can resemble the ESL (English as a second language) learner and tends to generate translations of low quality in terms of fluency and grammaticality, while the good translation model generally generates fluent and grammatically correct translations. With the pair of translation models, we can generate unlimited numbers of poor to good English sentence pairs from text in the source language (e.g., Chinese) of the translators. Our approach can generate various error-corrected patterns and nicely complement the other data synthesis approaches for GEC. Experimental results demonstrate the data generated by our approach can effectively help a GEC model to improve the performance and achieve the state-of-the-art single-model performance in BEA-19 and CoNLL-14 benchmark datasets.

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CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Zhangyin Feng | Daya Guo | Duyu Tang | Nan Duan | Xiaocheng Feng | Ming Gong | Linjun Shou | Bing Qin | Ting Liu | Daxin Jiang | Ming Zhou
Findings of the Association for Computational Linguistics: EMNLP 2020

We present CodeBERT, a bimodal pre-trained model for programming language (PL) and natural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language code search, code documentation generation, etc. We develop CodeBERT with Transformer-based neural architecture, and train it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators. This enables us to utilize both “bimodal” data of NL-PL pairs and “unimodal data, where the former provides input tokens for model training while the latter helps to learn better generators. We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters. Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation. Furthermore, to investigate what type of knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing, and evaluate in a zero-shot setting where parameters of pre-trained models are fixed. Results show that CodeBERT performs better than previous pre-trained models on NLPL probing.

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Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers
Shusheng Xu | Xingxing Zhang | Yi Wu | Furu Wei | Ming Zhou
Findings of the Association for Computational Linguistics: EMNLP 2020

Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph-based with sentences as nodes and edge weights measured by sentence similarities. In this work, we find that transformer attentions can be used to rank sentences for unsupervised extractive summarization. Specifically, we first pre-train a hierarchical transformer model using unlabeled documents only. Then we propose a method to rank sentences using sentence-level self-attentions and pre-training objectives. Experiments on CNN/DailyMail and New York Times datasets show our model achieves state-of-the-art performance on unsupervised summarization. We also find in experiments that our model is less dependent on sentence positions. When using a linear combination of our model and a recent unsupervised model explicitly modeling sentence positions, we obtain even better results.

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Scheduled DropHead: A Regularization Method for Transformer Models
Wangchunshu Zhou | Tao Ge | Furu Wei | Ming Zhou | Ke Xu
Findings of the Association for Computational Linguistics: EMNLP 2020

We introduce DropHead, a structured dropout method specifically designed for regularizing the multi-head attention mechanism which is a key component of transformer. In contrast to the conventional dropout mechanism which randomly drops units or connections, DropHead drops entire attention heads during training to prevent the multi-head attention model from being dominated by a small portion of attention heads. It can help reduce the risk of overfitting and allow the models to better benefit from the multi-head attention. Given the interaction between multi-headedness and training dynamics, we further propose a novel dropout rate scheduler to adjust the dropout rate of DropHead throughout training, which results in a better regularization effect. Experimental results demonstrate that our proposed approach can improve transformer models by 0.9 BLEU score on WMT14 En-De translation task and around 1.0 accuracy for various text classification tasks.

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ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training
Weizhen Qi | Yu Yan | Yeyun Gong | Dayiheng Liu | Nan Duan | Jiusheng Chen | Ruofei Zhang | Ming Zhou
Findings of the Association for Computational Linguistics: EMNLP 2020

This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of optimizing one-step-ahead prediction in the traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction that predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large-scale dataset (160GB), respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.

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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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A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction
Shuo Ren | Shujie Liu | Ming Zhou | Shuai Ma
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Unsupervised bilingual lexicon induction is the task of inducing word translations from monolingual corpora of two languages. Recent methods are mostly based on unsupervised cross-lingual word embeddings, the key to which is to find initial solutions of word translations, followed by the learning and refinement of mappings between the embedding spaces of two languages. However, previous methods find initial solutions just based on word-level information, which may be (1) limited and inaccurate, and (2) prone to contain some noise introduced by the insufficiently pre-trained embeddings of some words. To deal with those issues, in this paper, we propose a novel graph-based paradigm to induce bilingual lexicons in a coarse-to-fine way. We first build a graph for each language with its vertices representing different words. Then we extract word cliques from the graphs and map the cliques of two languages. Based on that, we induce the initial word translation solution with the central words of the aligned cliques. This coarse-to-fine approach not only leverages clique-level information, which is richer and more accurate, but also effectively reduces the bad effect of the noise in the pre-trained embeddings. Finally, we take the initial solution as the seed to learn cross-lingual embeddings, from which we induce bilingual lexicons. Experiments show that our approach improves the performance of bilingual lexicon induction compared with previous methods.

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A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation
Shuo Ren | Yu Wu | Shujie Liu | Ming Zhou | Shuai Ma
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The commonly used framework for unsupervised machine translation builds initial translation models of both translation directions, and then performs iterative back-translation to jointly boost their translation performance. The initialization stage is very important since bad initialization may wrongly squeeze the search space, and too much noise introduced in this stage may hurt the final performance. In this paper, we propose a novel retrieval and rewriting based method to better initialize unsupervised translation models. We first retrieve semantically comparable sentences from monolingual corpora of two languages and then rewrite the target side to minimize the semantic gap between the source and retrieved targets with a designed rewriting model. The rewritten sentence pairs are used to initialize SMT models which are used to generate pseudo data for two NMT models, followed by the iterative back-translation. Experiments show that our method can build better initial unsupervised translation models and improve the final translation performance by over 4 BLEU scores. Our code is released at https://github.com/Imagist-Shuo/RRforUNMT.git.

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A Simple and Effective Unified Encoder for Document-Level Machine Translation
Shuming Ma | Dongdong Zhang | Ming Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Most of the existing models for document-level machine translation adopt dual-encoder structures. The representation of the source sentences and the document-level contexts are modeled with two separate encoders. Although these models can make use of the document-level contexts, they do not fully model the interaction between the contexts and the source sentences, and can not directly adapt to the recent pre-training models (e.g., BERT) which encodes multiple sentences with a single encoder. In this work, we propose a simple and effective unified encoder that can outperform the baseline models of dual-encoder models in terms of BLEU and METEOR scores. Moreover, the pre-training models can further boost the performance of our proposed model.

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MIND: A Large-scale Dataset for News Recommendation
Fangzhao Wu | Ying Qiao | Jiun-Hung Chen | Chuhan Wu | Tao Qi | Jianxun Lian | Danyang Liu | Xing Xie | Jianfeng Gao | Winnie Wu | Ming Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

News recommendation is an important technique for personalized news service. Compared with product and movie recommendations which have been comprehensively studied, the research on news recommendation is much more limited, mainly due to the lack of a high-quality benchmark dataset. In this paper, we present a large-scale dataset named MIND for news recommendation. Constructed from the user click logs of Microsoft News, MIND contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. We demonstrate MIND a good testbed for news recommendation through a comparative study of several state-of-the-art news recommendation methods which are originally developed on different proprietary datasets. Our results show the performance of news recommendation highly relies on the quality of news content understanding and user interest modeling. Many natural language processing techniques such as effective text representation methods and pre-trained language models can effectively improve the performance of news recommendation. The MIND dataset will be available at https://msnews.github.io.

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Curriculum Pre-training for End-to-End Speech Translation
Chengyi Wang | Yu Wu | Shujie Liu | Ming Zhou | Zhenglu Yang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

End-to-end speech translation poses a heavy burden on the encoder because it has to transcribe, understand, and learn cross-lingual semantics simultaneously. To obtain a powerful encoder, traditional methods pre-train it on ASR data to capture speech features. However, we argue that pre-training the encoder only through simple speech recognition is not enough, and high-level linguistic knowledge should be considered. Inspired by this, we propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages. The difficulty of these courses is gradually increasing. Experiments show that our curriculum pre-training method leads to significant improvements on En-De and En-Fr speech translation benchmarks.

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Graph Neural News Recommendation with Unsupervised Preference Disentanglement
Linmei Hu | Siyong Xu | Chen Li | Cheng Yang | Chuan Shi | Nan Duan | Xing Xie | Ming Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

With the explosion of news information, personalized news recommendation has become very important for users to quickly find their interested contents. Most existing methods usually learn the representations of users and news from news contents for recommendation. However, they seldom consider high-order connectivity underlying the user-news interactions. Moreover, existing methods failed to disentangle a user’s latent preference factors which cause her clicks on different news. In this paper, we model the user-news interactions as a bipartite graph and propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement, named GNUD. Our model can encode high-order relationships into user and news representations by information propagation along the graph. Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability. A preference regularizer is also designed to force each disentangled subspace to independently reflect an isolated preference, improving the quality of the disentangled representations. Experimental results on real-world news datasets demonstrate that our proposed model can effectively improve the performance of news recommendation and outperform state-of-the-art news recommendation methods.

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Improving Neural Machine Translation with Soft Template Prediction
Jian Yang | Shuming Ma | Dongdong Zhang | Zhoujun Li | Ming Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Although neural machine translation (NMT) has achieved significant progress in recent years, most previous NMT models only depend on the source text to generate translation. Inspired by the success of template-based and syntax-based approaches in other fields, we propose to use extracted templates from tree structures as soft target templates to guide the translation procedure. In order to learn the syntactic structure of the target sentences, we adopt constituency-based parse tree to generate candidate templates. We incorporate the template information into the encoder-decoder framework to jointly utilize the templates and source text. Experiments show that our model significantly outperforms the baseline models on four benchmarks and demonstrates the effectiveness of soft target templates.

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LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network
Wanjun Zhong | Duyu Tang | Zhangyin Feng | Nan Duan | Ming Zhou | Ming Gong | Linjun Shou | Daxin Jiang | Jiahai Wang | Jian Yin
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Verifying the correctness of a textual statement requires not only semantic reasoning about the meaning of words, but also symbolic reasoning about logical operations like count, superlative, aggregation, etc. In this work, we propose LogicalFactChecker, a neural network approach capable of leveraging logical operations for fact checking. It achieves the state-of-the-art performance on TABFACT, a large-scale, benchmark dataset built for verifying a textual statement with semi-structured tables. This is achieved by a graph module network built upon the Transformer-based architecture. With a textual statement and a table as the input, LogicalFactChecker automatically derives a program (a.k.a. logical form) of the statement in a semantic parsing manner. A heterogeneous graph is then constructed to capture not only the structures of the table and the program, but also the connections between inputs with different modalities. Such a graph reveals the related contexts of each word in the statement, the table and the program. The graph is used to obtain graph-enhanced contextual representations of words in Transformer-based architecture. After that, a program-driven module network is further introduced to exploit the hierarchical structure of the program, where semantic compositionality is dynamically modeled along the program structure with a set of function-specific modules. Ablation experiments suggest that both the heterogeneous graph and the module network are important to obtain strong results.

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Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder
Daya Guo | Duyu Tang | Nan Duan | Jian Yin | Daxin Jiang | Ming Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context that is not explicitly provided, resulting in a context-independent semantic representation that struggles to support the generation. To address this, we propose an approach that automatically finds evidence for an event from a large text corpus, and leverages the evidence to guide the generation of inferential texts. Our approach works in an encoderdecoder manner and is equipped with Vector Quantised-Variational Autoencoder, where the encoder outputs representations from a distribution over discrete variables. Such discrete representations enable automatically selecting relevant evidence, which not only facilitates evidence-aware generation, but also provides a natural way to uncover rationales behind the generation. Our approach provides state-of-the-art performance on both Event2mind and Atomic datasets. More importantly, we find that with discrete representations, our model selectively uses evidence to generate different inferential texts.

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Reasoning Over Semantic-Level Graph for Fact Checking
Wanjun Zhong | Jingjing Xu | Duyu Tang | Zenan Xu | Nan Duan | Ming Zhou | Jiahai Wang | Jian Yin
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Fact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence. In this work, we present a method suitable for reasoning about the semantic-level structure of evidence. Unlike most previous works, which typically represent evidence sentences with either string concatenation or fusing the features of isolated evidence sentences, our approach operates on rich semantic structures of evidence obtained by semantic role labeling. We propose two mechanisms to exploit the structure of evidence while leveraging the advances of pre-trained models like BERT, GPT or XLNet. Specifically, using XLNet as the backbone, we first utilize the graph structure to re-define the relative distances of words, with the intuition that semantically related words should have short distances. Then, we adopt graph convolutional network and graph attention network to propagate and aggregate information from neighboring nodes on the graph. We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy. Our model is the state-of-the-art system in terms of both official evaluation metrics, namely claim verification accuracy and FEVER score.

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Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
Bo Zheng | Haoyang Wen | Yaobo Liang | Nan Duan | Wanxiang Che | Daxin Jiang | Ming Zhou | Ting Liu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Natural Questions is a new challenging machine reading comprehension benchmark with two-grained answers, which are a long answer (typically a paragraph) and a short answer (one or more entities inside the long answer). Despite the effectiveness of existing methods on this benchmark, they treat these two sub-tasks individually during training while ignoring their dependencies. To address this issue, we present a novel multi-grained machine reading comprehension framework that focuses on modeling documents at their hierarchical nature, which are different levels of granularity: documents, paragraphs, sentences, and tokens. We utilize graph attention networks to obtain different levels of representations so that they can be learned simultaneously. The long and short answers can be extracted from paragraph-level representation and token-level representation, respectively. In this way, we can model the dependencies between the two-grained answers to provide evidence for each other. We jointly train the two sub-tasks, and our experiments show that our approach significantly outperforms previous systems at both long and short answer criteria.

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Neural Deepfake Detection with Factual Structure of Text
Wanjun Zhong | Duyu Tang | Zenan Xu | Ruize Wang | Nan Duan | Ming Zhou | Jiahai Wang | Jian Yin
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coarse-grained representations. However, they struggle to capture factual structures of documents, which is a discriminative factor between machine-generated and human-written text according to our statistical analysis. To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text. Our approach represents the factual structure of a given document as an entity graph, which is further utilized to learn sentence representations with a graph neural network. Sentence representations are then composed to a document representation for making predictions, where consistent relations between neighboring sentences are sequentially modeled. Results of experiments on two public deepfake datasets show that our approach significantly improves strong base models built with RoBERTa. Model analysis further indicates that our model can distinguish the difference in the factual structure between machine-generated text and human-written text.

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Pre-training for Abstractive Document Summarization by Reinstating Source Text
Yanyan Zou | Xingxing Zhang | Wei Lu | Furu Wei | Ming Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Abstractive document summarization is usually modeled as a sequence-to-sequence (SEQ2SEQ) learning problem. Unfortunately, training large SEQ2SEQ based summarization models on limited supervised summarization data is challenging. This paper presents three sequence-to-sequence pre-training (in shorthand, STEP) objectives which allow us to pre-train a SEQ2SEQ based abstractive summarization model on unlabeled text. The main idea is that, given an input text artificially constructed from a document, a model is pre-trained to reinstate the original document. These objectives include sentence reordering, next sentence generation and masked document generation, which have close relations with the abstractive document summarization task. Experiments on two benchmark summarization datasets (i.e., CNN/DailyMail and New York Times) show that all three objectives can improve performance upon baselines. Compared to models pre-trained on large-scale data (larger than 160GB), our method, with only 19GB text for pre-training, achieves comparable results, which demonstrates its effectiveness.

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Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection
Ruize Wang | Duyu Tang | Nan Duan | Wanjun Zhong | Zhongyu Wei | Xuanjing Huang | Daxin Jiang | Ming Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language. The former refers to the logical consistency between coarse- and fine-grained predictions, which is used to regularize the training process with propositional Boolean expressions. The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection. Experiments show that our method achieves superior performance, demonstrating that leveraging declarative knowledge can help the model to make more accurate predictions.

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Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
Dayiheng Liu | Yeyun Gong | Jie Fu | Yu Yan | Jiusheng Chen | Jiancheng Lv | Nan Duan | Ming Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks. We treat the question data augmentation task as a constrained question rewriting problem to generate context-relevant, high-quality, and diverse question data samples. CRQDA utilizes a Transformer Autoencoder to map the original discrete question into a continuous embedding space. It then uses a pre-trained MRC model to revise the question representation iteratively with gradient-based optimization. Finally, the revised question representations are mapped back into the discrete space, which serve as additional question data. Comprehensive experiments on SQuAD 2.0, SQuAD 1.1 question generation, and QNLI tasks demonstrate the effectiveness of CRQDA.

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XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation
Yaobo Liang | Nan Duan | Yeyun Gong | Ning Wu | Fenfei Guo | Weizhen Qi | Ming Gong | Linjun Shou | Daxin Jiang | Guihong Cao | Xiaodong Fan | Ruofei Zhang | Rahul Agrawal | Edward Cui | Sining Wei | Taroon Bharti | Ying Qiao | Jiun-Hung Chen | Winnie Wu | Shuguang Liu | Fan Yang | Daniel Campos | Rangan Majumder | Ming Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks. Comparing to GLUE (Wang et al.,2019), which is labeled in English and includes natural language understanding tasks only, XGLUE has three main advantages: (1) it provides two corpora with different sizes for cross-lingual pre-training; (2) it provides 11 diversified tasks that cover both natural language understanding and generation scenarios; (3) for each task, it provides labeled data in multiple languages. We extend a recent cross-lingual pre-trained model Unicoder (Huang et al., 2019) to cover both understanding and generation tasks, which is evaluated on XGLUE as a strong baseline. We also evaluate the base versions (12-layer) of Multilingual BERT, XLM and XLM-R for comparison.

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Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction
Mengyun Chen | Tao Ge | Xingxing Zhang | Furu Wei | Ming Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with less than 50% time cost for inference.

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BERT-of-Theseus: Compressing BERT by Progressive Module Replacing
Canwen Xu | Wangchunshu Zhou | Tao Ge | Furu Wei | Ming Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we propose a novel model compression approach to effectively compress BERT by progressive module replacing. Our approach first divides the original BERT into several modules and builds their compact substitutes. Then, we randomly replace the original modules with their substitutes to train the compact modules to mimic the behavior of the original modules. We progressively increase the probability of replacement through the training. In this way, our approach brings a deeper level of interaction between the original and compact models. Compared to the previous knowledge distillation approaches for BERT compression, our approach does not introduce any additional loss function. Our approach outperforms existing knowledge distillation approaches on GLUE benchmark, showing a new perspective of model compression.

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Machine Reasoning: Technology, Dilemma and Future
Nan Duan | Duyu Tang | Ming Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Machine reasoning research aims to build interpretable AI systems that can solve problems or draw conclusions from what they are told (i.e. facts and observations) and already know (i.e. models, common sense and knowledge) under certain constraints. In this tutorial, we will (1) describe the motivation of this tutorial and give our definition on machine reasoning; (2) introduce typical machine reasoning frameworks, including symbolic reasoning, probabilistic reasoning, neural-symbolic reasoning and neural-evidence reasoning, and show their successful applications in real-world scenarios; (3) talk about the dilemma between black-box neural networks with state-of-the-art performance and machine reasoning approaches with better interpretability; (4) summarize the content of this tutorial and discuss possible future directions.

2019

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Explicit Cross-lingual Pre-training for Unsupervised Machine Translation
Shuo Ren | Yu Wu | Shujie Liu | Ming Zhou | Shuai Ma
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Pre-training has proven to be effective in unsupervised machine translation due to its ability to model deep context information in cross-lingual scenarios. However, the cross-lingual information obtained from shared BPE spaces is inexplicit and limited. In this paper, we propose a novel cross-lingual pre-training method for unsupervised machine translation by incorporating explicit cross-lingual training signals. Specifically, we first calculate cross-lingual n-gram embeddings and infer an n-gram translation table from them. With those n-gram translation pairs, we propose a new pre-training model called Cross-lingual Masked Language Model (CMLM), which randomly chooses source n-grams in the input text stream and predicts their translation candidates at each time step. Experiments show that our method can incorporate beneficial cross-lingual information into pre-trained models. Taking pre-trained CMLM models as the encoder and decoder, we significantly improve the performance of unsupervised machine translation.

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Asking Clarification Questions in Knowledge-Based Question Answering
Jingjing Xu | Yuechen Wang | Duyu Tang | Nan Duan | Pengcheng Yang | Qi Zeng | Ming Zhou | Xu Sun
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 ability to ask clarification questions is essential for knowledge-based question answering (KBQA) systems, especially for handling ambiguous phenomena. Despite its importance, clarification has not been well explored in current KBQA systems. Further progress requires supervised resources for training and evaluation, and powerful models for clarification-related text understanding and generation. In this paper, we construct a new clarification dataset, CLAQUA, with nearly 40K open-domain examples. The dataset supports three serial tasks: given a question, identify whether clarification is needed; if yes, generate a clarification question; then predict answers base on external user feedback. We provide representative baselines for these tasks and further introduce a coarse-to-fine model for clarification question generation. Experiments show that the proposed model achieves better performance than strong baselines. The further analysis demonstrates that our dataset brings new challenges and there still remain several unsolved problems, like reasonable automatic evaluation metrics for clarification question generation and powerful models for handling entity sparsity.

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Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks
Haoyang Huang | Yaobo Liang | Nan Duan | Ming Gong | Linjun Shou | Daxin Jiang | Ming Zhou
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 present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages. Comparing to similar efforts such as Multilingual BERT and XLM , three new cross-lingual pre-training tasks are proposed, including cross-lingual word recovery, cross-lingual paraphrase classification and cross-lingual masked language model. These tasks help Unicoder learn the mappings among different languages from more perspectives. We also find that doing fine-tuning on multiple languages together can bring further improvement. Experiments are performed on two tasks: cross-lingual natural language inference (XNLI) and cross-lingual question answering (XQA), where XLM is our baseline. On XNLI, 1.8% averaged accuracy improvement (on 15 languages) is obtained. On XQA, which is a new cross-lingual dataset built by us, 5.5% averaged accuracy improvement (on French and German) is obtained.

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Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension
Hangbo Bao | Li Dong | Furu Wei | Wenhui Wang | Nan Yang | Lei Cui | Songhao Piao | Ming Zhou
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

Most machine reading comprehension (MRC) models separately handle encoding and matching with different network architectures. In contrast, pretrained language models with Transformer layers, such as GPT (Radford et al., 2018) and BERT (Devlin et al., 2018), have achieved competitive performance on MRC. A research question that naturally arises is: apart from the benefits of pre-training, how many performance gain comes from the unified network architecture. In this work, we evaluate and analyze unifying encoding and matching components with Transformer for the MRC task. Experimental results on SQuAD show that the unified model outperforms previous networks that separately treat encoding and matching. We also introduce a metric to inspect whether a Transformer layer tends to perform encoding or matching. The analysis results show that the unified model learns different modeling strategies compared with previous manually-designed models.

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A Sequential Matching Framework for Multi-Turn Response Selection in Retrieval-Based Chatbots
Yu Wu | Wei Wu | Chen Xing | Can Xu | Zhoujun Li | Ming Zhou
Computational Linguistics, Volume 45, Issue 1 - March 2019

We study the problem of response selection for multi-turn conversation in retrieval-based chatbots. The task involves matching a response candidate with a conversation context, the challenges for which include how to recognize important parts of the context, and how to model the relationships among utterances in the context. Existing matching methods may lose important information in contexts as we can interpret them with a unified framework in which contexts are transformed to fixed-length vectors without any interaction with responses before matching. This motivates us to propose a new matching framework that can sufficiently carry important information in contexts to matching and model relationships among utterances at the same time. The new framework, which we call a sequential matching framework (SMF), lets each utterance in a context interact with a response candidate at the first step and transforms the pair to a matching vector. The matching vectors are then accumulated following the order of the utterances in the context with a recurrent neural network (RNN) that models relationships among utterances. Context-response matching is then calculated with the hidden states of the RNN. Under SMF, we propose a sequential convolutional network and sequential attention network and conduct experiments on two public data sets to test their performance. Experiment results show that both models can significantly outperform state-of-the-art matching methods. We also show that the models are interpretable with visualizations that provide us insights on how they capture and leverage important information in contexts for matching.

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Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing
Daya Guo | Duyu Tang | Nan Duan | Ming Zhou | Jian Yin
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we present an approach to incorporate retrieved datapoints as supporting evidence for context-dependent semantic parsing, such as generating source code conditioned on the class environment. Our approach naturally combines a retrieval model and a meta-learner, where the former learns to find similar datapoints from the training data, and the latter considers retrieved datapoints as a pseudo task for fast adaptation. Specifically, our retriever is a context-aware encoder-decoder model with a latent variable which takes context environment into consideration, and our meta-learner learns to utilize retrieved datapoints in a model-agnostic meta-learning paradigm for fast adaptation. We conduct experiments on CONCODE and CSQA datasets, where the context refers to class environment in JAVA codes and conversational history, respectively. We use sequence-to-action model as the base semantic parser, which performs the state-of-the-art accuracy on both datasets. Results show that both the context-aware retriever and the meta-learning strategy improve accuracy, and our approach performs better than retrieve-and-edit baselines.

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BERT-based Lexical Substitution
Wangchunshu Zhou | Tao Ge | Ke Xu | Furu Wei | Ming Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Previous studies on lexical substitution tend to obtain substitute candidates by finding the target word’s synonyms from lexical resources (e.g., WordNet) and then rank the candidates based on its contexts. These approaches have two limitations: (1) They are likely to overlook good substitute candidates that are not the synonyms of the target words in the lexical resources; (2) They fail to take into account the substitution’s influence on the global context of the sentence. To address these issues, we propose an end-to-end BERT-based lexical substitution approach which can propose and validate substitute candidates without using any annotated data or manually curated resources. Our approach first applies dropout to the target word’s embedding for partially masking the word, allowing BERT to take balanced consideration of the target word’s semantics and contexts for proposing substitute candidates, and then validates the candidates based on their substitution’s influence on the global contextualized representation of the sentence. Experiments show our approach performs well in both proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks.

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HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization
Xingxing Zhang | Furu Wei | Ming Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the hierarchical encoder with these inaccurate labels is challenging. Inspired by the recent work on pre-training transformer sentence encoders (Devlin et al., 2018), we propose Hibert (as shorthand for HIerachical Bidirectional Encoder Representations from Transformers) for document encoding and a method to pre-train it using unlabeled data. We apply the pre-trained Hibert to our summarization model and it outperforms its randomly initialized counterpart by 1.25 ROUGE on the CNN/Dailymail dataset and by 2.0 ROUGE on a version of New York Times dataset. We also achieve the state-of-the-art performance on these two datasets.

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Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study
Tao Ge | Xingxing Zhang | Furu Wei | Ming Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Sequence-to-sequence (seq2seq) models have achieved tremendous success in text generation tasks. However, there is no guarantee that they can always generate sentences without grammatical errors. In this paper, we present a preliminary empirical study on whether and how much automatic grammatical error correction can help improve seq2seq text generation. We conduct experiments across various seq2seq text generation tasks including machine translation, formality style transfer, sentence compression and simplification. Experiments show the state-of-the-art grammatical error correction system can improve the grammaticality of generated text and can bring task-oriented improvements in the tasks where target sentences are in a formal style.

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Dense Procedure Captioning in Narrated Instructional Videos
Botian Shi | Lei Ji | Yaobo Liang | Nan Duan | Peng Chen | Zhendong Niu | Ming Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Understanding narrated instructional videos is important for both research and real-world web applications. Motivated by video dense captioning, we propose a model to generate procedure captions from narrated instructional videos which are a sequence of step-wise clips with description. Previous works on video dense captioning learn video segments and generate captions without considering transcripts. We argue that transcripts in narrated instructional videos can enhance video representation by providing fine-grained complimentary and semantic textual information. In this paper, we introduce a framework to (1) extract procedures by a cross-modality module, which fuses video content with the entire transcript; and (2) generate captions by encoding video frames as well as a snippet of transcripts within each extracted procedure. Experiments show that our model can achieve state-of-the-art performance in procedure extraction and captioning, and the ablation studies demonstrate that both the video frames and the transcripts are important for the task.

2018

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Learning to Collaborate for Question Answering and Asking
Duyu Tang | Nan Duan | Zhao Yan | Zhirui Zhang | Yibo Sun | Shujie Liu | Yuanhua Lv | Ming Zhou
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Question answering (QA) and question generation (QG) are closely related tasks that could improve each other; however, the connection of these two tasks is not well explored in literature. In this paper, we give a systematic study that seeks to leverage the connection to improve both QA and QG. We present a training algorithm that generalizes both Generative Adversarial Network (GAN) and Generative Domain-Adaptive Nets (GDAN) under the question answering scenario. The two key ideas are improving the QG model with QA through incorporating additional QA-specific signal as the loss function, and improving the QA model with QG through adding artificially generated training instances. We conduct experiments on both document based and knowledge based question answering tasks. We have two main findings. Firstly, the performance of a QG model (e.g in terms of BLEU score) could be easily improved by a QA model via policy gradient. Secondly, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model. Learning when to regard generated questions as positive instances could bring performance boost.

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Generative Bridging Network for Neural Sequence Prediction
Wenhu Chen | Guanlin Li | Shuo Ren | Shujie Liu | Zhirui Zhang | Mu Li | Ming Zhou
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

In order to alleviate data sparsity and overfitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network). Unlike MLE directly maximizing the conditional likelihood, the bridge extends the point-wise ground truth to a bridge distribution conditioned on it, and the generator is optimized to minimize their KL-divergence. Three different GBNs, namely uniform GBN, language-model GBN and coaching GBN, are proposed to penalize confidence, enhance language smoothness and relieve learning burden. Experiments conducted on two recognized sequence prediction tasks (machine translation and abstractive text summarization) show that our proposed GBNs can yield significant improvements over strong baselines. Furthermore, by analyzing samples drawn from different bridges, expected influences on the generator are verified.

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Bidirectional Generative Adversarial Networks for Neural Machine Translation
Zhirui Zhang | Shujie Liu | Mu Li | Ming Zhou | Enhong Chen
Proceedings of the 22nd Conference on Computational Natural Language Learning

Generative Adversarial Network (GAN) has been proposed to tackle the exposure bias problem of Neural Machine Translation (NMT). However, the discriminator typically results in the instability of the GAN training due to the inadequate training problem: the search space is so huge that sampled translations are not sufficient for discriminator training. To address this issue and stabilize the GAN training, in this paper, we propose a novel Bidirectional Generative Adversarial Network for Neural Machine Translation (BGAN-NMT), which aims to introduce a generator model to act as the discriminator, whereby the discriminator naturally considers the entire translation space so that the inadequate training problem can be alleviated. To satisfy this property, generator and discriminator are both designed to model the joint probability of sentence pairs, with the difference that, the generator decomposes the joint probability with a source language model and a source-to-target translation model, while the discriminator is formulated as a target language model and a target-to-source translation model. To further leverage the symmetry of them, an auxiliary GAN is introduced and adopts generator and discriminator models of original one as its own discriminator and generator respectively. Two GANs are alternately trained to update the parameters. Experiment results on German-English and Chinese-English translation tasks demonstrate that our method not only stabilizes GAN training but also achieves significant improvements over baseline systems.

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EventWiki: A Knowledge Base of Major Events
Tao Ge | Lei Cui | Baobao Chang | Zhifang Sui | Furu Wei | Ming Zhou
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Triangular Architecture for Rare Language Translation
Shuo Ren | Wenhu Chen | Shujie Liu | Mu Li | Ming Zhou | Shuai Ma
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Neural Machine Translation (NMT) performs poor on the low-resource language pair (X,Z), especially when Z is a rare language. By introducing another rich language Y, we propose a novel triangular training architecture (TA-NMT) to leverage bilingual data (Y,Z) (may be small) and (X,Y) (can be rich) to improve the translation performance of low-resource pairs. In this triangular architecture, Z is taken as the intermediate latent variable, and translation models of Z are jointly optimized with an unified bidirectional EM algorithm under the goal of maximizing the translation likelihood of (X,Y). Empirical results demonstrate that our method significantly improves the translation quality of rare languages on MultiUN and IWSLT2012 datasets, and achieves even better performance combining back-translation methods.

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Semantic Parsing with Syntax- and Table-Aware SQL Generation
Yibo Sun | Duyu Tang | Nan Duan | Jianshu Ji | Guihong Cao | Xiaocheng Feng | Bing Qin | Ting Liu | Ming Zhou
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query word-by-word, however, a large portion of the generated results is incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest question- SQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0% to 74.4%.

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Neural Document Summarization by Jointly Learning to Score and Select Sentences
Qingyu Zhou | Nan Yang | Furu Wei | Shaohan Huang | Ming Zhou | Tiejun Zhao
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sentence scoring and sentence selection are two main steps in extractive document summarization systems. However, previous works treat them as two separated subtasks. In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences. It first reads the document sentences with a hierarchical encoder to obtain the representation of sentences. Then it builds the output summary by extracting sentences one by one. Different from previous methods, our approach integrates the selection strategy into the scoring model, which directly predicts the relative importance given previously selected sentences. Experiments on the CNN/Daily Mail dataset show that the proposed framework significantly outperforms the state-of-the-art extractive summarization models.

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Fluency Boost Learning and Inference for Neural Grammatical Error Correction
Tao Ge | Furu Wei | Ming Zhou
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Most of the neural sequence-to-sequence (seq2seq) models for grammatical error correction (GEC) have two limitations: (1) a seq2seq model may not be well generalized with only limited error-corrected data; (2) a seq2seq model may fail to completely correct a sentence with multiple errors through normal seq2seq inference. We attempt to address these limitations by proposing a fluency boost learning and inference mechanism. Fluency boosting learning generates fluency-boost sentence pairs during training, enabling the error correction model to learn how to improve a sentence’s fluency from more instances, while fluency boosting inference allows the model to correct a sentence incrementally with multiple inference steps until the sentence’s fluency stops increasing. Experiments show our approaches improve the performance of seq2seq models for GEC, achieving state-of-the-art results on both CoNLL-2014 and JFLEG benchmark datasets.

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Neural Open Information Extraction
Lei Cui | Furu Wei | Ming Zhou
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation. In this paper, we propose a neural Open IE approach with an encoder-decoder framework. Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system. An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.

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Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots
Yu Wu | Wei Wu | Zhoujun Li | Ming Zhou
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We propose a method that can leverage unlabeled data to learn a matching model for response selection in retrieval-based chatbots. The method employs a sequence-to-sequence architecture (Seq2Seq) model as a weak annotator to judge the matching degree of unlabeled pairs, and then performs learning with both the weak signals and the unlabeled data. Experimental results on two public data sets indicate that matching models get significant improvements when they are learned with the proposed method.

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Neural Latent Extractive Document Summarization
Xingxing Zhang | Mirella Lapata | Furu Wei | Ming Zhou
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Extractive summarization models need sentence level labels, which are usually created with rule-based methods since most summarization datasets only have document summary pairs. These labels might be suboptimal. We propose a latent variable extractive model, where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. During training, the loss can come directly from gold summaries. Experiments on CNN/Dailymail dataset show our latent extractive model outperforms a strong extractive baseline trained on rule-based labels and also performs competitively with several recent models.

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Question Generation from SQL Queries Improves Neural Semantic Parsing
Daya Guo | Yibo Sun | Duyu Tang | Nan Duan | Jian Yin | Hong Chi | James Cao | Peng Chen | Ming Zhou
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In this paper, we study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data. We conduct our study on WikiSQL, the largest hand-annotated semantic parsing dataset to date. First, we demonstrate that question generation is an effective method that empowers us to learn a state-of-the-art neural network based semantic parser with thirty percent of the supervised training data. Second, we show that applying question generation to the full supervised training data further improves the state-of-the-art model. In addition, we observe that there is a logarithmic relationship between the accuracy of a semantic parser and the amount of training data.

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Attention-Guided Answer Distillation for Machine Reading Comprehension
Minghao Hu | Yuxing Peng | Furu Wei | Zhen Huang | Dongsheng Li | Nan Yang | Ming Zhou
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Despite that current reading comprehension systems have achieved significant advancements, their promising performances are often obtained at the cost of making an ensemble of numerous models. Besides, existing approaches are also vulnerable to adversarial attacks. This paper tackles these problems by leveraging knowledge distillation, which aims to transfer knowledge from an ensemble model to a single model. We first demonstrate that vanilla knowledge distillation applied to answer span prediction is effective for reading comprehension systems. We then propose two novel approaches that not only penalize the prediction on confusing answers but also guide the training with alignment information distilled from the ensemble. Experiments show that our best student model has only a slight drop of 0.4% F1 on the SQuAD test set compared to the ensemble teacher, while running 12x faster during inference. It even outperforms the teacher on adversarial SQuAD datasets and NarrativeQA benchmark.

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Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition
Tao Ge | Qing Dou | Heng Ji | Lei Cui | Baobao Chang | Zhifang Sui | Furu Wei | Ming Zhou
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm. We use Burst Information Networks as media to represent text streams and present a simple yet effective network decipherment algorithm with diverse clues to decipher the networks for accurate text stream alignment. Experiments on Chinese-English news streams show our approach not only outperforms previous approaches on bilingual lexicon extraction from coordinated text streams but also can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining, which makes it promising to be a new paradigm for automatic language knowledge acquisition.

2017

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Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering
Wenzheng Feng | Yu Wu | Wei Wu | Zhoujun Li | Ming Zhou
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper presents the system in SemEval-2017 Task 3, Community Question Answering (CQA). We develop a ranking system that is capable of capturing semantic relations between text pairs with little word overlap. In addition to traditional NLP features, we introduce several neural network based matching features which enable our system to measure text similarity beyond lexicons. Our system significantly outperforms baseline methods and holds the second place in Subtask A and the fifth place in Subtask B, which demonstrates its efficacy on answer selection and question retrieval.

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Gated Self-Matching Networks for Reading Comprehension and Question Answering
Wenhui Wang | Nan Yang | Furu Wei | Baobao Chang | Ming Zhou
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we present the gated self-matching networks for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation. Then we propose a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage. We finally employ the pointer networks to locate the positions of answers from the passages. We conduct extensive experiments on the SQuAD dataset. The single model achieves 71.3% on the evaluation metrics of exact match on the hidden test set, while the ensemble model further boosts the results to 75.9%. At the time of submission of the paper, our model holds the first place on the SQuAD leaderboard for both single and ensemble model.

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Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots
Yu Wu | Wei Wu | Chen Xing | Ming Zhou | Zhoujun Li
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We study response selection for multi-turn conversation in retrieval based chatbots. Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among the utterances or important information in the context. We propose a sequential matching network (SMN) to address both problems. SMN first matches a response with each utterance in the context on multiple levels of granularity, and distills important matching information from each pair as a vector with convolution and pooling operations. The vectors are then accumulated in a chronological order through a recurrent neural network (RNN) which models relationships among the utterances. The final matching score is calculated with the hidden states of the RNN. Empirical study on two public data sets shows that SMN can significantly outperform state-of-the-art methods for response selection in multi-turn conversation.

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Sequence-to-Dependency Neural Machine Translation
Shuangzhi Wu | Dongdong Zhang | Nan Yang | Mu Li | Ming Zhou
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned. Inspired by the success of using syntactic knowledge of target language for improving statistical machine translation, in this paper we propose a novel Sequence-to-Dependency Neural Machine Translation (SD-NMT) method, in which the target word sequence and its corresponding dependency structure are jointly constructed and modeled, and this structure is used as context to facilitate word generations. Experimental results show that the proposed method significantly outperforms state-of-the-art baselines on Chinese-English and Japanese-English translation tasks.

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Selective Encoding for Abstractive Sentence Summarization
Qingyu Zhou | Nan Yang | Furu Wei | Ming Zhou
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate network, and an attention equipped decoder. The sentence encoder and decoder are built with recurrent neural networks. The selective gate network constructs a second level sentence representation by controlling the information flow from encoder to decoder. The second level representation is tailored for sentence summarization task, which leads to better performance. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. The experimental results show that the proposed selective encoding model outperforms the state-of-the-art baseline models.

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Chunk-based Decoder for Neural Machine Translation
Shonosuke Ishiwatari | Jingtao Yao | Shujie Liu | Mu Li | Ming Zhou | Naoki Yoshinaga | Masaru Kitsuregawa | Weijia Jia
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Chunks (or phrases) once played a pivotal role in machine translation. By using a chunk rather than a word as the basic translation unit, local (intra-chunk) and global (inter-chunk) word orders and dependencies can be easily modeled. The chunk structure, despite its importance, has not been considered in the decoders used for neural machine translation (NMT). In this paper, we propose chunk-based decoders for (NMT), each of which consists of a chunk-level decoder and a word-level decoder. The chunk-level decoder models global dependencies while the word-level decoder decides the local word order in a chunk. To output a target sentence, the chunk-level decoder generates a chunk representation containing global information, which the word-level decoder then uses as a basis to predict the words inside the chunk. Experimental results show that our proposed decoders can significantly improve translation performance in a WAT ‘16 English-to-Japanese translation task.

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SuperAgent: A Customer Service Chatbot for E-commerce Websites
Lei Cui | Shaohan Huang | Furu Wei | Chuanqi Tan | Chaoqun Duan | Ming Zhou
Proceedings of ACL 2017, System Demonstrations

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Entity Linking for Queries by Searching Wikipedia Sentences
Chuanqi Tan | Furu Wei | Pengjie Ren | Weifeng Lv | Ming Zhou
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-probability, context-matching, word embeddings, and relatedness among candidate entities as well as their related entities, to rank the candidates under a regression based framework. The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. First, it can greatly reduce the number of candidate entities by filtering out irrelevant entities with the words in the query. Second, we can obtain the query sensitive prior probability in addition to the static link-probability derived from all Wikipedia articles. We conduct experiments on two benchmark datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ dataset. Experimental results show that our method outperforms state-of-the-art systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ dataset.

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Question Generation for Question Answering
Nan Duan | Duyu Tang | Peng Chen | Ming Zhou
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper presents how to generate questions from given passages using neural networks, where large scale QA pairs are automatically crawled and processed from Community-QA website, and used as training data. The contribution of the paper is 2-fold: First, two types of question generation approaches are proposed, one is a retrieval-based method using convolution neural network (CNN), the other is a generation-based method using recurrent neural network (RNN); Second, we show how to leverage the generated questions to improve existing question answering systems. We evaluate our question generation method for the answer sentence selection task on three benchmark datasets, including SQuAD, MS MARCO, and WikiQA. Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achieved.

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Stack-based Multi-layer Attention for Transition-based Dependency Parsing
Zhirui Zhang | Shujie Liu | Mu Li | Ming Zhou | Enhong Chen
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Although sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a comparable performance gain as in other state-of-the-art methods, such as stack-LSTM and head selection. In this paper, we propose a stack-based multi-layer attention model for seq2seq learning to better leverage structural linguistics information. In our method, two binary vectors are used to track the decoding stack in transition-based parsing, and multi-layer attention is introduced to capture multiple word dependencies in partial trees. We conduct experiments on PTB and CTB datasets, and the results show that our proposed model achieves state-of-the-art accuracy and significant improvement in labeled precision with respect to the baseline seq2seq model.

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Learning to Generate Product Reviews from Attributes
Li Dong | Shaohan Huang | Furu Wei | Mirella Lapata | Ming Zhou | Ke Xu
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Automatically generating product reviews is a meaningful, yet not well-studied task in sentiment analysis. Traditional natural language generation methods rely extensively on hand-crafted rules and predefined templates. This paper presents an attention-enhanced attribute-to-sequence model to generate product reviews for given attribute information, such as user, product, and rating. The attribute encoder learns to represent input attributes as vectors. Then, the sequence decoder generates reviews by conditioning its output on these vectors. We also introduce an attention mechanism to jointly generate reviews and align words with input attributes. The proposed model is trained end-to-end to maximize the likelihood of target product reviews given the attributes. We build a publicly available dataset for the review generation task by leveraging the Amazon book reviews and their metadata. Experiments on the dataset show that our approach outperforms baseline methods and the attention mechanism significantly improves the performance of our model.

2016

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News Stream Summarization using Burst Information Networks
Tao Ge | Lei Cui | Baobao Chang | Sujian Li | Ming Zhou | Zhifang Sui
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Solving and Generating Chinese Character Riddles
Chuanqi Tan | Furu Wei | Li Dong | Weifeng Lv | Ming Zhou
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents
Zhao Yan | Nan Duan | Junwei Bao | Peng Chen | Ming Zhou | Zhoujun Li | Jianshe Zhou
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Knowledge-Based Semantic Embedding for Machine Translation
Chen Shi | Shujie Liu | Shuo Ren | Shi Feng | Mu Li | Ming Zhou | Xu Sun | Houfeng Wang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Redundancy-Aware Sentence Regression Framework for Extractive Summarization
Pengjie Ren | Furu Wei | Zhumin Chen | Jun Ma | Ming Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Existing sentence regression methods for extractive summarization usually model sentence importance and redundancy in two separate processes. They first evaluate the importance f(s) of each sentence s and then select sentences to generate a summary based on both the importance scores and redundancy among sentences. In this paper, we propose to model importance and redundancy simultaneously by directly evaluating the relative importance f(s|S) of a sentence s given a set of selected sentences S. Specifically, we present a new framework to conduct regression with respect to the relative gain of s given S calculated by the ROUGE metric. Besides the single sentence features, additional features derived from the sentence relations are incorporated. Experiments on the DUC 2001, 2002 and 2004 multi-document summarization datasets show that the proposed method outperforms state-of-the-art extractive summarization approaches.

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Detecting Context Dependent Messages in a Conversational Environment
Chaozhuo Li | Yu Wu | Wei Wu | Chen Xing | Zhoujun Li | Ming Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

While automatic response generation for building chatbot systems has drawn a lot of attention recently, there is limited understanding on when we need to consider the linguistic context of an input text in the generation process. The task is challenging, as messages in a conversational environment are short and informal, and evidence that can indicate a message is context dependent is scarce. After a study of social conversation data crawled from the web, we observed that some characteristics estimated from the responses of messages are discriminative for identifying context dependent messages. With the characteristics as weak supervision, we propose using a Long Short Term Memory (LSTM) network to learn a classifier. Our method carries out text representation and classifier learning in a unified framework. Experimental results show that the proposed method can significantly outperform baseline methods on accuracy of classification.

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Constraint-Based Question Answering with Knowledge Graph
Junwei Bao | Nan Duan | Zhao Yan | Ming Zhou | Tiejun Zhao
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work. Most questions in them are ‘simple’ questions which can be answered based on a single relation in the knowledge base. Such data-sets lack the capability of evaluating KBQA systems on complicated questions. Motivated by this issue, we release a new data-set, namely ComplexQuestions, aiming to measure the quality of KBQA systems on ‘multi-constraint’ questions which require multiple knowledge base relations to get the answer. Beside, we propose a novel systematic KBQA approach to solve multi-constraint questions. Compared to state-of-the-art methods, our approach not only obtains comparable results on the two existing benchmark data-sets, but also achieves significant improvements on the ComplexQuestions.

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Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation
Shi Feng | Shujie Liu | Nan Yang | Mu Li | Ming Zhou | Kenny Q. Zhu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In neural machine translation, the attention mechanism facilitates the translation process by producing a soft alignment between the source sentence and the target sentence. However, without dedicated distortion and fertility models seen in traditional SMT systems, the learned alignment may not be accurate, which can lead to low translation quality. In this paper, we propose two novel models to improve attention-based neural machine translation. We propose a recurrent attention mechanism as an implicit distortion model, and a fertility conditioned decoder as an implicit fertility model. We conduct experiments on large-scale Chinese–English translation tasks. The results show that our models significantly improve both the alignment and translation quality compared to the original attention mechanism and several other variations.

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Event Detection with Burst Information Networks
Tao Ge | Lei Cui | Baobao Chang | Zhifang Sui | Ming Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Retrospective event detection is an important task for discovering previously unidentified events in a text stream. In this paper, we propose two fast centroid-aware event detection models based on a novel text stream representation – Burst Information Networks (BINets) for addressing the challenge. The BINets are time-aware, efficient and can be easily analyzed for identifying key information (centroids). These advantages allow the BINet-based approaches to achieve the state-of-the-art performance on multiple datasets, demonstrating the efficacy of BINets for the task of event detection.

2015

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Hierarchical Recurrent Neural Network for Document Modeling
Rui Lin | Shujie Liu | Muyun Yang | Mu Li | Ming Zhou | Sheng Li
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Cross-lingual Sentiment Lexicon Learning With Bilingual Word Graph Label Propagation
Dehong Gao | Furu Wei | Wenjie Li | Xiaohua Liu | Ming Zhou
Computational Linguistics, Volume 41, Issue 1 - March 2015

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A Statistical Parsing Framework for Sentiment Classification
Li Dong | Furu Wei | Shujie Liu | Ming Zhou | Ke Xu
Computational Linguistics, Volume 41, Issue 2 - June 2015

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Splusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of Tweets
Li Dong | Furu Wei | Yichun Yin | Ming Zhou | Ke Xu
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Question Answering over Freebase with Multi-Column Convolutional Neural Networks
Li Dong | Furu Wei | Ming Zhou | Ke Xu
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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Efficient Disfluency Detection with Transition-based Parsing
Shuangzhi Wu | Dongdong Zhang | Ming Zhou | Tiejun Zhao
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 Dependency-Based Neural Network for Relation Classification
Yang Liu | Furu Wei | Sujian Li | Heng Ji | Ming Zhou | Houfeng Wang
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Learning Summary Prior Representation for Extractive Summarization
Ziqiang Cao | Furu Wei | Sujian Li | Wenjie Li | Ming Zhou | Houfeng Wang
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach
Duyu Tang | Furu Wei | Bing Qin | Ming Zhou | Ting Liu
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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A Lexicalized Reordering Model for Hierarchical Phrase-based Translation
Hailong Cao | Dongdong Zhang | Mu Li | Ming Zhou | Tiejun Zhao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Soft Dependency Matching for Hierarchical Phrase-based Machine Translation
Hailong Cao | Dongdong Zhang | Ming Zhou | Tiejun Zhao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Coooolll: A Deep Learning System for Twitter Sentiment Classification
Duyu Tang | Furu Wei | Bing Qin | Ting Liu | Ming Zhou
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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A Joint Segmentation and Classification Framework for Sentiment Analysis
Duyu Tang | Furu Wei | Bing Qin | Li Dong | Ting Liu | Ming Zhou
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Joint Relational Embeddings for Knowledge-based Question Answering
Min-Chul Yang | Nan Duan | Ming Zhou | Hae-Chang Rim
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Bilingually-constrained Phrase Embeddings for Machine Translation
Jiajun Zhang | Shujie Liu | Mu Li | Ming Zhou | Chengqing Zong
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning Topic Representation for SMT with Neural Networks
Lei Cui | Dongdong Zhang | Shujie Liu | Qiming Chen | Mu Li | Ming Zhou | Muyun Yang
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Knowledge-Based Question Answering as Machine Translation
Junwei Bao | Nan Duan | Ming Zhou | Tiejun Zhao
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Recursive Recurrent Neural Network for Statistical Machine Translation
Shujie Liu | Nan Yang | Mu Li | Ming Zhou
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
Duyu Tang | Furu Wei | Nan Yang | Ming Zhou | Ting Liu | Bing Qin
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification
Li Dong | Furu Wei | Chuanqi Tan | Duyu Tang | Ming Zhou | Ke Xu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Efficient Collective Entity Linking with Stacking
Zhengyan He | Shujie Liu | Yang Song | Mu Li | Ming Zhou | Houfeng Wang
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Multi-Domain Adaptation for SMT Using Multi-Task Learning
Lei Cui | Xilun Chen | Dongdong Zhang | Shujie Liu | Mu Li | Ming Zhou
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Word Alignment Modeling with Context Dependent Deep Neural Network
Nan Yang | Shujie Liu | Mu Li | Ming Zhou | Nenghai Yu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Entity Linking for Tweets
Xiaohua Liu | Yitong Li | Haocheng Wu | Ming Zhou | Furu Wei | Yi Lu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Machine Translation Detection from Monolingual Web-Text
Yuki Arase | Ming Zhou
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning Entity Representation for Entity Disambiguation
Zhengyan He | Shujie Liu | Mu Li | Ming Zhou | Longkai Zhang | Houfeng Wang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Paraphrasing Adaptation for Web Search Ranking
Chenguang Wang | Nan Duan | Ming Zhou | Ming Zhang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Bilingual Data Cleaning for SMT using Graph-based Random Walk
Lei Cui | Dongdong Zhang | Shujie Liu | Mu Li | Ming Zhou
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Forced Derivation Tree based Model Training to Statistical Machine Translation
Nan Duan | Mu Li | Ming Zhou
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Re-training Monolingual Parser Bilingually for Syntactic SMT
Shujie Liu | Chi-Ho Li | Mu Li | Ming Zhou
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Twitter Topic Summarization by Ranking Tweets using Social Influence and Content Quality
Yajuan Duan | Zhumin Chen | Furu Wei | Ming Zhou | Heung-Yeung Shum
Proceedings of COLING 2012

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Graph-Based Multi-Tweet Summarization using Social Signals
Xiaohua Liu | Yitong Li | Furu Wei | Ming Zhou
Proceedings of COLING 2012

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Lost in Translations? Building Sentiment Lexicons using Context Based Machine Translation
Xinfan Meng | Furu Wei | Ge Xu | Longkai Zhang | Xiaohua Liu | Ming Zhou | Houfeng Wang
Proceedings of COLING 2012: Posters

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Learning Translation Consensus with Structured Label Propagation
Shujie Liu | Chi-Ho Li | Mu Li | Ming Zhou
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Joint Inference of Named Entity Recognition and Normalization for Tweets
Xiaohua Liu | Ming Zhou | Xiangyang Zhou | Zhongyang Fu | Furu Wei
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Cross-Lingual Mixture Model for Sentiment Classification
Xinfan Meng | Furu Wei | Xiaohua Liu | Ming Zhou | Ge Xu | Houfeng Wang
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Joint Learning of a Dual SMT System for Paraphrase Generation
Hong Sun | Ming Zhou
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Translation Model Size Reduction for Hierarchical Phrase-based Statistical Machine Translation
Seung-Wook Lee | Dongdong Zhang | Mu Li | Ming Zhou | Hae-Chang Rim
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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QuickView: NLP-based Tweet Search
Xiaohua Liu | Furu Wei | Ming Zhou | QuickView Team Microsoft
Proceedings of the ACL 2012 System Demonstrations

2011

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Statistic Machine Translation Boosted with Spurious Word Deletion
Shujie Liu | Chi-Ho Li | Ming Zhou
Proceedings of Machine Translation Summit XIII: Papers

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Function Word Generation in Statistical Machine Translation Systems
Lei Cui | Dongdong Zhang | Mu Li | Ming Zhou
Proceedings of Machine Translation Summit XIII: Papers

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A Unified SMT Framework Combining MIRA and MERT
Shujie Liu | Chi-Ho Li | Ming Zhou
Proceedings of Machine Translation Summit XIII: Papers

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Improving Phrase Extraction via MBR Phrase Scoring and Pruning
Nan Duan | Mu Li | Ming Zhou | Lei Cui
Proceedings of Machine Translation Summit XIII: Papers

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ENGtube: an Integrated Subtitle Environment for ESL
Chi-Ho Li | Shujie Liu | Chenguang Wang | Ming Zhou
Proceedings of Machine Translation Summit XIII: System Presentations

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Target-dependent Twitter Sentiment Classification
Long Jiang | Mo Yu | Ming Zhou | Xiaohua Liu | Tiejun Zhao
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Recognizing Named Entities in Tweets
Xiaohua Liu | Shaodian Zhang | Furu Wei | Ming Zhou
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Hypothesis Mixture Decoding for Statistical Machine Translation
Nan Duan | Mu Li | Ming Zhou
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Engkoo: Mining the Web for Language Learning
Matthew R. Scott | Xiaohua Liu | Ming Zhou | Microsoft Engkoo Team
Proceedings of the ACL-HLT 2011 System Demonstrations

2010

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Discriminative Pruning for Discriminative ITG Alignment
Shujie Liu | Chi-Ho Li | Ming Zhou
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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A Joint Rule Selection Model for Hierarchical Phrase-Based Translation
Lei Cui | Dongdong Zhang | Mu Li | Ming Zhou | Tiejun Zhao
Proceedings of the ACL 2010 Conference Short Papers

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SRL-Based Verb Selection for ESL
Xiaohua Liu | Bo Han | Kuan Li | Stephan Hyeonjun Stiller | Ming Zhou
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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An Empirical Study on Learning to Rank of Tweets
Yajuan Duan | Long Jiang | Tao Qin | Ming Zhou | Heung-Yeung Shum
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Translation Model Generalization using Probability Averaging for Machine Translation
Nan Duan | Hong Sun | Ming Zhou
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Mixture Model-based Minimum Bayes Risk Decoding using Multiple Machine Translation Systems
Nan Duan | Mu Li | Dongdong Zhang | Ming Zhou
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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An Empirical Study on Web Mining of Parallel Data
Gumwon Hong | Chi-Ho Li | Ming Zhou | Hae-Chang Rim
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Adaptive Development Data Selection for Log-linear Model in Statistical Machine Translation
Mu Li | Yinggong Zhao | Dongdong Zhang | Ming Zhou
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Semantic Role Labeling for News Tweets
Xiaohua Liu | Kuan Li | Bo Han | Ming Zhou | Long Jiang | Zhongyang Xiong | Changning Huang
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Hybrid Decoding: Decoding with Partial Hypotheses Combination over Multiple SMT Systems
Lei Cui | Dongdong Zhang | Mu Li | Ming Zhou | Tiejun Zhao
Coling 2010: Posters

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Collective Semantic Role Labeling on Open News Corpus by Leveraging Redundancy
Xiaohua Liu | Kuan Li | Bo Han | Ming Zhou | Long Jiang | Daniel Tse | Zhongyang Xiong
Coling 2010: Posters

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Improved Discriminative ITG Alignment using Hierarchical Phrase Pairs and Semi-supervised Training
Shujie Liu | Chi-Ho Li | Ming Zhou
Coling 2010: Posters

2009

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Collaborative Decoding: Partial Hypothesis Re-ranking Using Translation Consensus between Decoders
Mu Li | Nan Duan | Dongdong Zhang | Chi-Ho Li | Ming Zhou
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Mining Bilingual Data from the Web with Adaptively Learnt Patterns
Long Jiang | Shiquan Yang | Ming Zhou | Xiaohua Liu | Qingsheng Zhu
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Exploiting Bilingual Information to Improve Web Search
Wei Gao | John Blitzer | Ming Zhou | Kam-Fai Wong
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Better Synchronous Binarization for Machine Translation
Tong Xiao | Mu Li | Dongdong Zhang | Jingbo Zhu | Ming Zhou
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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The Feature Subspace Method for SMT System Combination
Nan Duan | Mu Li | Tong Xiao | Ming Zhou
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Generating Chinese Couplets and Quatrain Using a Statistical Approach
Ming Zhou | Long Jiang | Jing He
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1

2008

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An Empirical Study in Source Word Deletion for Phrase-Based Statistical Machine Translation
Chi-Ho Li | Hailei Zhang | Dongdong Zhang | Mu Li | Ming Zhou
Proceedings of the Third Workshop on Statistical Machine Translation

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Generating Chinese Couplets using a Statistical MT Approach
Long Jiang | Ming Zhou
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Diagnostic Evaluation of Machine Translation Systems Using Automatically Constructed Linguistic Check-Points
Ming Zhou | Bo Wang | Shujie Liu | Mu Li | Dongdong Zhang | Tiejun Zhao
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Improved Sentence Alignment on Parallel Web Pages Using a Stochastic Tree Alignment Model
Lei Shi | Ming Zhou
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Measure Word Generation for English-Chinese SMT Systems
Dongdong Zhang | Mu Li | Nan Duan | Chi-Ho Li | Ming Zhou
Proceedings of ACL-08: HLT

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Combining Multiple Resources to Improve SMT-based Paraphrasing Model
Shiqi Zhao | Cheng Niu | Ming Zhou | Ting Liu | Sheng Li
Proceedings of ACL-08: HLT

2007

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Detection of Non-Native Sentences Using Machine-Translated Training Data
John Lee | Ming Zhou | Xiaohua Liu
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

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Sentence Level Machine Translation Evaluation as a Ranking
Yang Ye | Ming Zhou | Chin-Yew Lin
Proceedings of the Second Workshop on Statistical Machine Translation

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Improving Query Spelling Correction Using Web Search Results
Qing Chen | Mu Li | Ming Zhou
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Low-Quality Product Review Detection in Opinion Summarization
Jingjing Liu | Yunbo Cao | Chin-Yew Lin | Yalou Huang | Ming Zhou
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Phrase Reordering Model Integrating Syntactic Knowledge for SMT
Dongdong Zhang | Mu Li | Chi-Ho Li | Ming Zhou
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Detecting Erroneous Sentences using Automatically Mined Sequential Patterns
Guihua Sun | Xiaohua Liu | Gao Cong | Ming Zhou | Zhongyang Xiong | John Lee | Chin-Yew Lin
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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A Probabilistic Approach to Syntax-based Reordering for Statistical Machine Translation
Chi-Ho Li | Minghui Li | Dongdong Zhang | Mu Li | Ming Zhou | Yi Guan
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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A DOM Tree Alignment Model for Mining Parallel Data from the Web
Lei Shi | Cheng Niu | Ming Zhou | Jianfeng Gao
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Exploring Distributional Similarity Based Models for Query Spelling Correction
Mu Li | Muhua Zhu | Yang Zhang | Ming Zhou
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Reranking Answers for Definitional QA Using Language Modeling
Yi Chen | Ming Zhou | Shilong Wang
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

2005

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Resume Information Extraction with Cascaded Hybrid Model
Kun Yu | Gang Guan | Ming Zhou
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

2004

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Improving Word Alignment Models using Structured Monolingual Corpora
Wei Wang | Ming Zhou
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

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A New Approach for English-Chinese Named Entity Alignment
Donghui Feng | Yajuan Lv | Ming Zhou
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

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Collocation Translation Acquisition Using Monolingual Corpora
Yajuan Lü | Ming Zhou
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

2003

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A unified statistical model for generalized translation memory system
Jin-Xia Huang | Wei Wang | Ming Zhou
Proceedings of Machine Translation Summit IX: Papers

We introduced, for Translation Memory System, a statistical framework, which unifies the different phases in a Translation Memory System by letting them constrain each other, and enables Translation Memory System a statistical qualification. Compared to traditional Translation Memory Systems, our model operates at a fine grained sub-sentential level such that it improves the translation coverage. Compared with other approaches that exploit sub-sentential benefits, it unifies the processes of source string segmentation, best example selection, and translation generation by making them constrain each other via the statistical confidence of each step. We realized this framework into a prototype system. Compared with an existing product Translation Memory System, our system exhibits obviously better performance in the "assistant quality metric" and gains improvements in the range of 26.3% to 55.1% in the "translation efficiency metric".

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A Class-based Language Model Approach to Chinese Named Entity Identification
Jian Sun | Ming Zhou | Jianfeng Gao
International Journal of Computational Linguistics & Chinese Language Processing, Volume 8, Number 2, August 2003

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Synonymous Collocation Extraction Using Translation Information
Hua Wu | Ming Zhou
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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Optimizing Synonym Extraction Using Monolingual and Bilingual Resources
Hua Wu | Ming Zhou
Proceedings of the Second International Workshop on Paraphrasing

2002

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Structure Alignment Using Bilingual Chunking
Wei Wang | Ming Zhou | Jin-Xia Huang | Chang-Ning Huang
COLING 2002: The 19th International Conference on Computational Linguistics

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Chinese Named Entity Identification Using Class-based Language Model
Jian Sun | Jianfeng Gao | Lei Zhang | Ming Zhou | Changning Huang
COLING 2002: The 19th International Conference on Computational Linguistics

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An Automatic Evaluation Method for Localization Oriented Lexicalised EBMT System
Jianmin Yao | Ming Zhou | Tiejun Zhao | Hao Yu | Sheng Li
COLING 2002: The 19th International Conference on Computational Linguistics

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Self-Organizing Chinese and Japanese Semantic Maps
Qing Ma | Min Zhang | Masaki Murata | Ming Zhou | Hitoshi Isahara
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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Improving Translation Selection with a New Translation Model Trained by Independent Monolingual Corpora
Ming Zhou | Yuan Ding | Changning Huang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 6, Number 1, February 2001: Special Issue on Natural Language Processing Researches in MSRA

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Automatic Translation Template Acquisition Based on Bilingual Structure Alignment
Yajuan Lu | Ming Zhou | Sheng Li | Changning Huang | Tiejun Zhao
International Journal of Computational Linguistics & Chinese Language Processing, Volume 6, Number 1, February 2001: Special Issue on Natural Language Processing Researches in MSRA

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Improving the Effectiveness of Information Retrieval with Clustering and Fusion
Jian Zhang | Jianfeng Gao | Ming Zhou | Jiaxing Wang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 6, Number 1, February 2001: Special Issue on Natural Language Processing Researches in MSRA

2000

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A Unified Statistical Model for the Identification of English BaseNP
Endong Xun | Changning Huang | Ming Zhou
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics

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Automatic Detecting/Correcting Errors in Chinese Text by an Approximate Word-Matching Algorithm
Lei Zhang | Ming Zhou | Changning Huang | Haihua Pan
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics

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PENS: A Machine-aided English Writing System for Chinese Users
Ting Liu | Ming Zhou | Jianfeng Gao | Endong Xun | Changning Huang
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics

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A Block-Based Robust Dependency Parser for Unrestricted Chinese Text
Ming Zhou
Second Chinese Language Processing Workshop

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Extraction of Chinese Compound Words - An Experimental Study on a Very Large Corpus
Jian Zhang | Jianfeng Gao | Ming Zhou
Second Chinese Language Processing Workshop

1995

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Are Statistics-Based Approaches Good Enough For NLP? A Case Study Of Maximal-Length NP Extraction In Mandarin Chinese
Wenjie Li | Haihua Pan | Ming Zhou | Kam-Fai Wong | Vincent Lum
Proceedings of Rocling VIII Computational Linguistics Conference VIII

1994

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An Efficient Syntactic Tagging Tool for Corpora
Ming Zhou | Changning Huang
COLING 1994 Volume 2: The 15th International Conference on Computational Linguistics

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