Kun Xu


2022

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Zero-shot Cross-lingual Conversational Semantic Role Labeling
Han Wu | Haochen Tan | Kun Xu | Shuqi Liu | Lianwei Wu | Linqi Song
Findings of the Association for Computational Linguistics: NAACL 2022

While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. To avoid expensive data collection and error-propagation of translation-based methods, we present a simple but effective approach to perform zero-shot cross-lingual CSRL.Our model implicitly learns language-agnostic, conversational structure-aware and semantically rich representations with the hierarchical encoders and elaborately designed pre-training objectives. Experimental results show that our model outperforms all baselines by large margins on two newly collected English CSRL test sets. More importantly, we confirm the usefulness of CSRL to non-Chinese conversational tasks such as the question-in-context rewriting task in English and the multi-turn dialogue response generation tasks in English, German and Japanese by incorporating the CSRL information into the downstream conversation-based models. We believe this finding is significant and will facilitate the research of non-Chinese dialogue tasks which suffer the problems of ellipsis and anaphora.

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Variational Graph Autoencoding as Cheap Supervision for AMR Coreference Resolution
Irene Li | Linfeng Song | Kun Xu | Dong Yu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Coreference resolution over semantic graphs like AMRs aims to group the graph nodes that represent the same entity. This is a crucial step for making document-level formal semantic representations. With annotated data on AMR coreference resolution, deep learning approaches have recently shown great potential for this task, yet they are usually data hunger and annotations are costly. We propose a general pretraining method using variational graph autoencoder (VGAE) for AMR coreference resolution, which can leverage any general AMR corpus and even automatically parsed AMR data. Experiments on benchmarks show that the pretraining approach achieves performance gains of up to 6% absolute F1 points. Moreover, our model significantly improves on the previous state-of-the-art model by up to 11% F1.

2021

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CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
Han Wu | Kun Xu | Linqi Song
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding. However, it remains a major challenge for existing CSRL parser to handle conversational structural information. In this paper, we present a simple and effective architecture for CSRL which aims to address this problem. Our model is based on a conversational structure aware graph network which explicitly encodes the speaker dependent information. We also propose a multi-task learning method to further improve the model. Experimental results on benchmark datasets show that our model with our proposed training objectives significantly outperforms previous baselines.

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DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling
Baojun Wang | Zhao Zhang | Kun Xu | Guang-Yuan Hao | Yuyang Zhang | Lifeng Shang | Linlin Li | Xiao Chen | Xin Jiang | Qun Liu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks. However, previous works commonly have difficulty dealing with large-scale dynamic lexicons which often cause excessive matching noise and problems of frequent updates. In this paper, we propose DyLex, a plug-in lexicon incorporation approach for BERT based sequence labeling tasks. Instead of leveraging embeddings of words in the lexicon as in conventional methods, we adopt word-agnostic tag embeddings to avoid re-training the representation while updating the lexicon. Moreover, we employ an effective supervised lexical knowledge denoising method to smooth out matching noise. Finally, we introduce a col-wise attention based knowledge fusion mechanism to guarantee the pluggability of the proposed framework. Experiments on ten datasets of three tasks show that the proposed framework achieves new SOTA, even with very large scale lexicons.

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Exophoric Pronoun Resolution in Dialogues with Topic Regularization
Xintong Yu | Hongming Zhang | Yangqiu Song | Changshui Zhang | Kun Xu | Dong Yu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Resolving pronouns to their referents has long been studied as a fundamental natural language understanding problem. Previous works on pronoun coreference resolution (PCR) mostly focus on resolving pronouns to mentions in text while ignoring the exophoric scenario. Exophoric pronouns are common in daily communications, where speakers may directly use pronouns to refer to some objects present in the environment without introducing the objects first. Although such objects are not mentioned in the dialogue text, they can often be disambiguated by the general topics of the dialogue. Motivated by this, we propose to jointly leverage the local context and global topics of dialogues to solve the out-of-text PCR problem. Extensive experiments demonstrate the effectiveness of adding topic regularization for resolving exophoric pronouns.

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RAST: Domain-Robust Dialogue Rewriting as Sequence Tagging
Jie Hao | Linfeng Song | Liwei Wang | Kun Xu | Zhaopeng Tu | Dong Yu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this task suffer from the robustness issue, i.e., performances drop dramatically when testing on a different dataset. We address this robustness issue by proposing a novel sequence-tagging-based model so that the search space is significantly reduced, yet the core of this task is still well covered. As a common issue of most tagging models for text generation, the model’s outputs may lack fluency. To alleviate this issue, we inject the loss signal from BLEU or GPT-2 under a REINFORCE framework. Experiments show huge improvements of our model over the current state-of-the-art systems when transferring to another dataset.

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Instance-adaptive training with noise-robust losses against noisy labels
Lifeng Jin | Linfeng Song | Kun Xu | Dong Yu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In order to alleviate the huge demand for annotated datasets for different tasks, many recent natural language processing datasets have adopted automated pipelines for fast-tracking usable data. However, model training with such datasets poses a challenge because popular optimization objectives are not robust to label noise induced in the annotation generation process. Several noise-robust losses have been proposed and evaluated on tasks in computer vision, but they generally use a single dataset-wise hyperparamter to control the strength of noise resistance. This work proposes novel instance-adaptive training frameworks to change single dataset-wise hyperparameters of noise resistance in such losses to be instance-wise. Such instance-wise noise resistance hyperparameters are predicted by special instance-level label quality predictors, which are trained along with the main classification models. Experiments on noisy and corrupted NLP datasets show that proposed instance-adaptive training frameworks help increase the noise-robustness provided by such losses, promoting the use of the frameworks and associated losses in NLP models trained with noisy data.

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Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion
Jiayuan Huang | Yangkai Du | Shuting Tao | Kun Xu | Pengtao Xie
Transactions of the Association for Computational Linguistics, Volume 9

Abstract To develop commonsense-grounded NLP applications, a comprehensive and accurate commonsense knowledge graph (CKG) is needed. It is time-consuming to manually construct CKGs and many research efforts have been devoted to the automatic construction of CKGs. Previous approaches focus on generating concepts that have direct and obvious relationships with existing concepts and lack an capability to generate unobvious concepts. In this work, we aim to bridge this gap. We propose a general graph-to-paths pretraining framework that leverages high-order structures in CKGs to capture high-order relationships between concepts. We instantiate this general framework to four special cases: long path, path-to-path, router, and graph-node-path. Experiments on two datasets demonstrate the effectiveness of our methods. The code will be released via the public GitHub repository.

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Domain-Adaptive Pretraining Methods for Dialogue Understanding
Han Wu | Kun Xu | Linfeng Song | Lifeng Jin | Haisong Zhang | Linqi Song
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adaptive pretraining objectives on downstream tasks. In particular, three objectives, including a novel objective focusing on modeling predicate-argument relations, are evaluated on two challenging dialogue understanding tasks. Experimental results demonstrate that domain-adaptive pretraining with proper objectives can significantly improve the performance of a strong baseline on these tasks, achieving the new state-of-the-art performances.

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On the Generation of Medical Dialogs for COVID-19
Meng Zhou | Zechen Li | Bowen Tan | Guangtao Zeng | Wenmian Yang | Xuehai He | Zeqian Ju | Subrato Chakravorty | Shu Chen | Xingyi Yang | Yichen Zhang | Qingyang Wu | Zhou Yu | Kun Xu | Eric Xing | Pengtao Xie
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms have a pressing need to consult doctors. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialog system that can provide COVID19-related consultations. We collected two dialog datasets – CovidDialog – (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. While the largest of their kind, these two datasets are still relatively small compared with general-domain dialog datasets. Training complex dialog generation models on small datasets bears high risk of overfitting. To alleviate overfitting, we develop a multi-task learning approach, which regularizes the data-deficient dialog generation task with a masked token prediction task. Experiments on the CovidDialog datasets demonstrate the effectiveness of our approach. We perform both human evaluation and automatic evaluation of dialogs generated by our method. Results show that the generated responses are promising in being doctor-like, relevant to conversation history, clinically informative and correct. The code and the data are available at https://github.com/UCSD-AI4H/COVID-Dialogue.

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TexSmart: A System for Enhanced Natural Language Understanding
Lemao Liu | Haisong Zhang | Haiyun Jiang | Yangming Li | Enbo Zhao | Kun Xu | Linfeng Song | Suncong Zheng | Botong Zhou | Dick Zhu | Xiao Feng | Tao Chen | Tao Yang | Dong Yu | Feng Zhang | ZhanHui Kang | Shuming Shi
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

This paper introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. Compared to most previous publicly available text understanding systems and tools, TexSmart holds some unique features. First, the NER function of TexSmart supports over 1,000 entity types, while most other public tools typically support several to (at most) dozens of entity types. Second, TexSmart introduces new semantic analysis functions like semantic expansion and deep semantic representation, that are absent in most previous systems. Third, a spectrum of algorithms (from very fast algorithms to those that are relatively slow but more accurate) are implemented for one function in TexSmart, to fulfill the requirements of different academic and industrial applications. The adoption of unsupervised or weakly-supervised algorithms is especially emphasized, with the goal of easily updating our models to include fresh data with less human annotation efforts.

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Joint Coreference Resolution and Character Linking for Multiparty Conversation
Jiaxin Bai | Hongming Zhang | Yangqiu Song | Kun Xu
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Character linking, the task of linking mentioned people in conversations to the real world, is crucial for understanding the conversations. For the efficiency of communication, humans often choose to use pronouns (e.g., “she”) or normal entities (e.g., “that girl”) rather than named entities (e.g., “Rachel”) in the spoken language, which makes linking those mentions to real people a much more challenging than a regular entity linking task. To address this challenge, we propose to incorporate the richer context from the coreference relations among different mentions to help the linking. On the other hand, considering that finding coreference clusters itself is not a trivial task and could benefit from the global character information, we propose to jointly solve these two tasks. Specifically, we propose Cˆ2, the joint learning model of Coreference resolution and Character linking. The experimental results demonstrate that Cˆ2 can significantly outperform previous works on both tasks. Further analyses are conducted to analyze the contribution of all modules in the proposed model and the effect of all hyper-parameters.

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Video-aided Unsupervised Grammar Induction
Songyang Zhang | Linfeng Song | Lifeng Jin | Kun Xu | Dong Yu | Jiebo Luo
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video. Existing methods of multi-modal grammar induction focus on grammar induction from text-image pairs, with promising results showing that the information from static images is useful in induction. However, videos provide even richer information, including not only static objects but also actions and state changes useful for inducing verb phrases. In this paper, we explore rich features (e.g. action, object, scene, audio, face, OCR and speech) from videos, taking the recent Compound PCFG model as the baseline. We further propose a Multi-Modal Compound PCFG model (MMC-PCFG) to effectively aggregate these rich features from different modalities. Our proposed MMC-PCFG is trained end-to-end and outperforms each individual modality and previous state-of-the-art systems on three benchmarks, i.e. DiDeMo, YouCook2 and MSRVTT, confirming the effectiveness of leveraging video information for unsupervised grammar induction.

2020

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

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

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

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

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Semantic Role Labeling Guided Multi-turn Dialogue ReWriter
Kun Xu | Haochen Tan | Linfeng Song | Han Wu | Haisong Zhang | Linqi Song | Dong Yu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting ride of the noises is essential to improve its performance. Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words. In this paper, we propose to use semantic role labeling (SRL), which highlights the core semantic information of who did what to whom, to provide additional guidance for the rewriter model. Experiments show that this information significantly improves a RoBERTa-based model that already outperforms previous state-of-the-art systems.

2019

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Multiplex Word Embeddings for Selectional Preference Acquisition
Hongming Zhang | Jiaxin Bai | Yan Song | Kun Xu | Changlong Yu | Yangqiu Song | Wilfred Ng | Dong Yu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Conventional word embeddings represent words with fixed vectors, which are usually trained based on co-occurrence patterns among words. In doing so, however, the power of such representations is limited, where the same word might be functionalized separately under different syntactic relations. To address this limitation, one solution is to incorporate relational dependencies of different words into their embeddings. Therefore, in this paper, we propose a multiplex word embedding model, which can be easily extended according to various relations among words. As a result, each word has a center embedding to represent its overall semantics, and several relational embeddings to represent its relational dependencies. Compared to existing models, our model can effectively distinguish words with respect to different relations without introducing unnecessary sparseness. Moreover, to accommodate various relations, we use a small dimension for relational embeddings and our model is able to keep their effectiveness. Experiments on selectional preference acquisition and word similarity demonstrate the effectiveness of the proposed model, and a further study of scalability also proves that our embeddings only need 1/20 of the original embedding size to achieve better performance.

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Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering
Kun Xu | Yuxuan Lai | Yansong Feng | Zhiguo Wang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Traditional Key-value Memory Neural Networks (KV-MemNNs) are proved to be effective to support shallow reasoning over a collection of documents in domain specific Question Answering or Reading Comprehension tasks. However, extending KV-MemNNs to Knowledge Based Question Answering (KB-QA) is not trivia, which should properly decompose a complex question into a sequence of queries against the memory, and update the query representations to support multi-hop reasoning over the memory. In this paper, we propose a novel mechanism to enable conventional KV-MemNNs models to perform interpretable reasoning for complex questions. To achieve this, we design a new query updating strategy to mask previously-addressed memory information from the query representations, and introduce a novel STOP strategy to avoid invalid or repeated memory reading without strong annotation signals. This also enables KV-MemNNs to produce structured queries and work in a semantic parsing fashion. Experimental results on benchmark datasets show that our solution, trained with question-answer pairs only, can provide conventional KV-MemNNs models with better reasoning abilities on complex questions, and achieve state-of-art performances.

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

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

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Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
Haoyu Wang | Ming Tan | Mo Yu | Shiyu Chang | Dakuo Wang | Kun Xu | Xiaoxiao Guo | Saloni Potdar
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Many approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this makes difficult to scale to longer paragraphs and larger text corpora. In this work, we focus on the task of multiple relation extractions by encoding the paragraph only once. We build our solution upon the pre-trained self-attentive models (Transformer), where we first add a structured prediction layer to handle extraction between multiple entity pairs, then enhance the paragraph embedding to capture multiple relational information associated with each entity with entity-aware attention. We show that our approach is not only scalable but can also perform state-of-the-art on the standard benchmark ACE 2005.

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Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
Kun Xu | Liwei Wang | Mo Yu | Yansong Feng | Yan Song | Zhiguo Wang | Dong Yu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs. In this paper, we introduce the topic entity graph, a local sub-graph of an entity, to represent entities with their contextual information in KG. From this view, the KB-alignment task can be formulated as a graph matching problem; and we further propose a graph-attention based solution, which first matches all entities in two topic entity graphs, and then jointly model the local matching information to derive a graph-level matching vector. Experiments show that our model outperforms previous state-of-the-art methods by a large margin.

2018

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Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
Kun Xu | Lingfei Wu | Zhiguo Wang | Mo Yu | Liwei Chen | Vadim Sheinin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency or constituent trees. In this paper, we first propose to use the syntactic graph to represent three types of syntactic information, i.e., word order, dependency and constituency features; then employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.

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SQL-to-Text Generation with Graph-to-Sequence Model
Kun Xu | Lingfei Wu | Zhiguo Wang | Yansong Feng | Vadim Sheinin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this paper, we propose a graph-to-sequence model to encode the global structure information into node embeddings. This model can effectively learn the correlation between the SQL query pattern and its interpretation. Experimental results on the WikiSQL dataset and Stackoverflow dataset show that our model outperforms the Seq2Seq and Tree2Seq baselines, achieving the state-of-the-art performance.

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Word Mover’s Embedding: From Word2Vec to Document Embedding
Lingfei Wu | Ian En-Hsu Yen | Kun Xu | Fangli Xu | Avinash Balakrishnan | Pin-Yu Chen | Pradeep Ravikumar | Michael J. Witbrock
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings. Recent work has demonstrated that a distance measure between documents called Word Mover’s Distance (WMD) that aligns semantically similar words, yields unprecedented KNN classification accuracy. However, WMD is expensive to compute, and it is hard to extend its use beyond a KNN classifier. In this paper, we propose the Word Mover’s Embedding (WME), a novel approach to building an unsupervised document (sentence) embedding from pre-trained word embeddings. In our experiments on 9 benchmark text classification datasets and 22 textual similarity tasks, the proposed technique consistently matches or outperforms state-of-the-art techniques, with significantly higher accuracy on problems of short length.

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QUEST: A Natural Language Interface to Relational Databases
Vadim Sheinin | Elahe Khorashani | Hangu Yeo | Kun Xu | Ngoc Phuoc An Vo | Octavian Popescu
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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Question Answering on Freebase via Relation Extraction and Textual Evidence
Kun Xu | Siva Reddy | Yansong Feng | Songfang Huang | Dongyan Zhao
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Hybrid Question Answering over Knowledge Base and Free Text
Kun Xu | Yansong Feng | Songfang Huang | Dongyan Zhao
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Recent trend in question answering (QA) systems focuses on using structured knowledge bases (KBs) to find answers. While these systems are able to provide more precise answers than information retrieval (IR) based QA systems, the natural incompleteness of KB inevitably limits the question scope that the system can answer. In this paper, we present a hybrid question answering (hybrid-QA) system which exploits both structured knowledge base and free text to answer a question. The main challenge is to recognize the meaning of a question using these two resources, i.e., structured KB and free text. To address this, we map relational phrases to KB predicates and textual relations simultaneously, and further develop an integer linear program (ILP) model to infer on these candidates and provide a globally optimal solution. Experiments on benchmark datasets show that our system can benefit from both structured KB and free text, outperforming the state-of-the-art systems.

2015

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Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
Kun Xu | Yansong Feng | Songfang Huang | Dongyan Zhao
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Semantic Interpretation of Superlative Expressions via Structured Knowledge Bases
Sheng Zhang | Yansong Feng | Songfang Huang | Kun Xu | Zhe Han | Dongyan Zhao
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)