Duyu Tang


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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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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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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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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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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.


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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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Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base
Tao Shen | Xiubo Geng | Tao Qin | Daya Guo | Duyu Tang | Nan Duan | Guodong Long | Daxin Jiang
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 consider the problem of conversational question answering over a large-scale knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent neural semantic parsing based approaches usually decompose the task into several subtasks and then solve them sequentially, which leads to following issues: 1) errors in earlier subtasks will be propagated and negatively affect downstream ones; and 2) each subtask cannot naturally share supervision signals with others. To tackle these issues, we propose an innovative multi-task learning framework where a pointer-equipped semantic parsing model is designed to resolve coreference in conversations, and naturally empower joint learning with a novel type-aware entity detection model. The proposed framework thus enables shared supervisions and alleviates the effect of error propagation. Experiments on a large-scale conversational question answering dataset containing 1.6M question answering pairs over 12.8M entities show that the proposed framework improves overall F1 score from 67% to 79% compared with previous state-of-the-art work.

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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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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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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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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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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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Aspect Level Sentiment Classification with Deep Memory Network
Duyu Tang | Bing Qin | Ting Liu
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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English-Chinese Knowledge Base Translation with Neural Network
Xiaocheng Feng | Duyu Tang | Bing Qin | Ting Liu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Knowledge base (KB) such as Freebase plays an important role for many natural language processing tasks. English knowledge base is obviously larger and of higher quality than low resource language like Chinese. To expand Chinese KB by leveraging English KB resources, an effective way is to translate English KB (source) into Chinese (target). In this direction, two major challenges are to model triple semantics and to build a robust KB translator. We address these challenges by presenting a neural network approach, which learns continuous triple representation with a gated neural network. Accordingly, source triples and target triples are mapped in the same semantic vector space. We build a new dataset for English-Chinese KB translation from Freebase, and compare with several baselines on it. Experimental results show that the proposed method improves translation accuracy compared with baseline methods. We show that adaptive composition model improves standard solution such as neural tensor network in terms of translation accuracy.

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Effective LSTMs for Target-Dependent Sentiment Classification
Duyu Tang | Bing Qin | Xiaocheng Feng | Ting Liu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment polarity of a sentence towards the target. Therefore, it is desirable to integrate the connections between target word and context words when building a learning system. In this paper, we develop two target dependent long short-term memory (LSTM) models, where target information is automatically taken into account. We evaluate our methods on a benchmark dataset from Twitter. Empirical results show that modeling sentence representation with standard LSTM does not perform well. Incorporating target information into LSTM can significantly boost the classification accuracy. The target-dependent LSTM models achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.

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A Language-Independent Neural Network for Event Detection
Xiaocheng Feng | Lifu Huang | Duyu Tang | Heng Ji | Bing Qin | Ting Liu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
Duyu Tang | Bing Qin | Ting Liu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Learning Semantic Representations of Users and Products for Document Level Sentiment Classification
Duyu Tang | Bing Qin | Ting Liu
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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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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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)

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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 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)