Chuan Wang


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MidMed: Towards Mixed-Type Dialogues for Medical Consultation
Xiaoming Shi | Zeming Liu | Chuan Wang | Haitao Leng | Kui Xue | Xiaofan Zhang | Shaoting Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Most medical dialogue systems assume that patients have clear goals (seeking a diagnosis, medicine querying, etc.) before medical consultation. However, in many real situations, due to the lack of medical knowledge, it is usually difficult for patients to determine clear goals with all necessary slots. In this paper, we identify this challenge as how to construct medical consultation dialogue systems to help patients clarify their goals. For further study, we create a novel human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering four dialogue types: task-oriented dialogue for diagnosis, recommendation, QA, and chitchat. MidMed covers four departments (otorhinolaryngology, ophthalmology, skin, and digestive system), with 8,309 dialogues. Furthermore, we build benchmarking baselines on MidMed and propose an instruction-guiding medical dialogue generation framework, termed InsMed, to handle mixed-type dialogues. Experimental results show the effectiveness of InsMed.


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The LAIX Systems in the BEA-2019 GEC Shared Task
Ruobing Li | Chuan Wang | Yefei Zha | Yonghong Yu | Shiman Guo | Qiang Wang | Yang Liu | Hui Lin
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

In this paper, we describe two systems we developed for the three tracks we have participated in the BEA-2019 GEC Shared Task. We investigate competitive classification models with bi-directional recurrent neural networks (Bi-RNN) and neural machine translation (NMT) models. For different tracks, we use ensemble systems to selectively combine the NMT models, the classification models, and some rules, and demonstrate that an ensemble solution can effectively improve GEC performance over single systems. Our GEC systems ranked the first in the Unrestricted Track, and the third in both the Restricted Track and the Low Resource Track.


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Transition-Based Chinese AMR Parsing
Chuan Wang | Bin Li | Nianwen Xue
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

This paper presents the first AMR parser built on the Chinese AMR bank. By applying a transition-based AMR parsing framework to Chinese, we first investigate how well the transitions first designed for English AMR parsing generalize to Chinese and provide a comparative analysis between the transitions for English and Chinese. We then perform a detailed error analysis to identify the major challenges in Chinese AMR parsing that we hope will inform future research in this area.


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Getting the Most out of AMR Parsing
Chuan Wang | Nianwen Xue
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment. We first build a Bidirectional LSTM based concept identifier that is able to incorporate richer contextual information to learn sparse AMR concept labels. We then extend an HMM-based word-to-concept alignment model with graph distance distortion and a rescoring method during decoding to incorporate the structural information in the AMR graph. We show integrating the two components into an existing AMR parser results in consistently better performance over the state of the art on various datasets.

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Addressing the Data Sparsity Issue in Neural AMR Parsing
Xiaochang Peng | Chuan Wang | Daniel Gildea | Nianwen Xue
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Neural attention models have achieved great success in different NLP tasks. However, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we describe a sequence-to-sequence model for AMR parsing and present different ways to tackle the data sparsity problem. We show that our methods achieve significant improvement over a baseline neural attention model and our results are also competitive against state-of-the-art systems that do not use extra linguistic resources.

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Discourse Segmentation for Building a RST Chinese Treebank
Shuyuan Cao | Nianwen Xue | Iria da Cunha | Mikel Iruskieta | Chuan Wang
Proceedings of the 6th Workshop on Recent Advances in RST and Related Formalisms


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CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR Parser
Chuan Wang | Sameer Pradhan | Xiaoman Pan | Heng Ji | Nianwen Xue
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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CoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing
Nianwen Xue | Hwee Tou Ng | Sameer Pradhan | Attapol Rutherford | Bonnie Webber | Chuan Wang | Hongmin Wang
Proceedings of the CoNLL-16 shared task


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Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers
Chuan Wang | Nianwen Xue | Sameer Pradhan
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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A Transition-based Algorithm for AMR Parsing
Chuan Wang | Nianwen Xue | Sameer Pradhan
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies