Chao-Chun Liang


2020

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A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers
Shen-yun Miao | Chao-Chun Liang | Keh-Yih Su
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers. Existing MWP corpora for studying AI progress remain limited either in language usage patterns or in problem types. We thus present a new English MWP corpus with 2,305 MWPs that cover more text patterns and most problem types taught in elementary school. Each MWP is annotated with its problem type and grade level (for indicating the level of difficulty). Furthermore, we propose a metric to measure the lexicon usage diversity of a given MWP corpus, and demonstrate that ASDiv is more diverse than existing corpora. Experiments show that our proposed corpus reflects the true capability of MWP solvers more faithfully.

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 Tag-based English Math Word Problem Solver with Understanding, Reasoning and Explanation
Chao-Chun Liang | Kuang-Yi Hsu | Chien-Tsung Huang | Chung-Min Li | Shen-Yu Miao | Keh-Yih Su
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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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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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