Yi-Chung Lin


2021

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How Fast can BERT Learn Simple Natural Language Inference?
Yi-Chung Lin | Keh-Yih Su
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

This paper empirically studies whether BERT can really learn to conduct natural language inference (NLI) without utilizing hidden dataset bias; and how efficiently it can learn if it could. This is done via creating a simple entailment judgment case which involves only binary predicates in plain English. The results show that the learning process of BERT is very slow. However, the efficiency of learning can be greatly improved (data reduction by a factor of 1,500) if task-related features are added. This suggests that domain knowledge greatly helps when conducting NLI with neural networks.

2018

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Supporting Evidence Retrieval for Answering Yes/No Questions
Meng-Tse Wu | Yi-Chung Lin | Keh-Yih Su
International Journal of Computational Linguistics & Chinese Language Processing, Volume 23, Number 2, December 2018

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是非題之支持證據檢索 (Supporting Evidence Retrieval for Answering Yes/No Questions) [In Chinese]
Meng-Tse Wu | Yi-Chung Lin | Keh-Yih Su
Proceedings of the 30th Conference on Computational Linguistics and Speech Processing (ROCLING 2018)

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A Meaning-Based Statistical English Math Word Problem Solver
Chao-Chun Liang | Yu-Shiang Wong | Yi-Chung Lin | Keh-Yih Su
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or mechanical pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.

2016

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A Meaning-based English Math Word Problem Solver with Understanding, Reasoning and Explanation
Chao-Chun Liang | Shih-Hong Tsai | Ting-Yun Chang | Yi-Chung Lin | Keh-Yih Su
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

This paper presents a meaning-based statistical math word problem (MWP) solver with understanding, reasoning and explanation. It comprises a web user interface and pipelined modules for analysing the text, transforming both body and question parts into their logic forms, and then performing inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating the extracted math quantity with its associated syntactic and semantic information (which specifies the physical meaning of that quantity). Those role-tags are then used to identify the desired operands and filter out irrelevant quantities (so that the answer can be obtained precisely). Since the physical meaning of each quantity is explicitly represented with those role-tags and used in the inference process, the proposed approach could explain how the answer is obtained in a human comprehensible way.

2015

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Designing a Tag-Based Statistical Math Word Problem Solver with Reasoning and Explanation
Chien-Tsung Huang | Yi-Chung Lin | Chao-Chun Liang | Kuang-Yi Hsu | Shen-Yun Miao | Wei-Yun Ma | Lun-Wen Ku | Churn-Jung Liau | Keh-Yih Su
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)

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Explanation Generation for a Math Word Problem Solver
Chien-Tsung Huang | Yi-Chung Lin | Keh-Yih Su
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)

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Designing a Tag-Based Statistical Math Word Problem Solver with Reasoning and Explanation
Yi-Chung Lin | Chao-Chun Liang | Kuang-Yi Hsu | Chien-Tsung Huang | Shen-Yun Miao | Wei-Yun Ma | Lun-Wei Ku | Churn-Jung Liau | Keh-Yih Su
International Journal of Computational Linguistics & Chinese Language Processing, Volume 20, Number 2, December 2015 - Special Issue on Selected Papers from ROCLING XXVII

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Explanation Generation for a Math Word Problem Solver
Chien-Tsung Huang | Yi-Chung Lin | Keh-Yih Su
International Journal of Computational Linguistics & Chinese Language Processing, Volume 20, Number 2, December 2015 - Special Issue on Selected Papers from ROCLING XXVII

2002

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Probabilistic Named Entity Verification
Yi-Chung Lin | Peng-Hsiang Hung
COLING-02: COMPUTERM 2002: Second International Workshop on Computational Terminology

1999

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A Level-Synchronous Approach to Ill-formed Sentence Parsing and Error Recovery
Yi-Chung Lin | Keh-Yih Su
International Journal of Computational Linguistics & Chinese Language Processing, Volume 4, Number 1, February 1999

1998

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Error Recovery in Natural Language Parsing With a Level-Synchronous Approach
Yi-Chung Lin | Keh-Yih Su
Proceedings of Research on Computational Linguistics Conference XI

1997

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A Level-synchronous Approach to Ill-formed Sentence Parsing
Yi-Chung Lin | Keh-Yih Su
Proceedings of the 10th Research on Computational Linguistics International Conference

1995

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Automatic Construction of a Chinese Electronic Dictionary
Jing-Shin Chang | Yi-Chung Lin | Keh-Yih Su
Third Workshop on Very Large Corpora

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Robust Learning, Smoothing, and Parameter Tying on Syntactic Ambiguity Resolution
Tung-Hui Chiang | Yi-Chung Lin | Keh-Yih Su
Computational Linguistics, Volume 21, Number 3, September 1995

1994

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AUTOMATIC MODEL REFINEMENT - with an application to tagging
Yi-Chung Lin | Tung-Hui Chiang | Keh-Yih Su
COLING 1994 Volume 1: The 15th International Conference on Computational Linguistics

1992

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Syntactic Ambiguity Resolution Using A Discrimination and Robustness Oriented Adaptive Learning Algorithm
Tung-Hui Chiang | Yi-Chung Lin | Keh-Yih Su
COLING 1992 Volume 1: The 14th International Conference on Computational Linguistics

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Discrimination Oriented Probabilistic Tagging
Yi-Chung Lin | Tung-Hui Chiang | Keh-Yih Su
Proceedings of Rocling V Computational Linguistics Conference V