Yantao Du


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A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension
Jiahua Liu | Wan Wei | Maosong Sun | Hao Chen | Yantao Du | Dekang Lin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The task of machine reading comprehension (MRC) has evolved from answering simple questions from well-edited text to answering real questions from users out of web data. In the real-world setting, full-body text from multiple relevant documents in the top search results are provided as context for questions from user queries, including not only questions with a single, short, and factual answer, but also questions about reasons, procedures, and opinions. In this case, multiple answers could be equally valid for a single question and each answer may occur multiple times in the context, which should be taken into consideration when we build MRC system. We propose a multi-answer multi-task framework, in which different loss functions are used for multiple reference answers. Minimum Risk Training is applied to solve the multi-occurrence problem of a single answer. Combined with a simple heuristic passage extraction strategy for overlong documents, our model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09.


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Parsing for Grammatical Relations via Graph Merging
Weiwei Sun | Yantao Du | Xiaojun Wan
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

This paper is concerned with building deep grammatical relation (GR) analysis using data-driven approach. To deal with this problem, we propose graph merging, a new perspective, for building flexible dependency graphs: Constructing complex graphs via constructing simple subgraphs. We discuss two key problems in this perspective: (1) how to decompose a complex graph into simple subgraphs, and (2) how to combine subgraphs into a coherent complex graph. Experiments demonstrate the effectiveness of graph merging. Our parser reaches state-of-the-art performance and is significantly better than two transition-based parsers.


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Transition-Based Parsing for Deep Dependency Structures
Xun Zhang | Yantao Du | Weiwei Sun | Xiaojun Wan
Computational Linguistics, Volume 42, Issue 3 - September 2016


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Peking: Building Semantic Dependency Graphs with a Hybrid Parser
Yantao Du | Fan Zhang | Xun Zhang | Weiwei Sun | Xiaojun Wan
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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A Data-Driven, Factorization Parser for CCG Dependency Structures
Yantao Du | Weiwei Sun | Xiaojun Wan
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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Peking: Profiling Syntactic Tree Parsing Techniques for Semantic Graph Parsing
Yantao Du | Fan Zhang | Weiwei Sun | Xiaojun Wan
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Grammatical Relations in Chinese: GB-Ground Extraction and Data-Driven Parsing
Weiwei Sun | Yantao Du | Xin Kou | Shuoyang Ding | Xiaojun Wan
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)