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.

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). 2018.

A goal-oriented meaning-based statistical framework is presented in this paper to solve the math word problem that requires multiple arithmetic operations with understanding, reasoning and explanation. It first analyzes and transforms sentences into their meaning-based logical forms, which represent the associated context of each quantity with role-tags (e.g., nsubj, verb, etc.). Logic forms with role-tags provide a flexible and simple way to specify the physical meaning of a quantity. Afterwards, the main-goal of the problem is decomposed recursively into its associated sub-goals. For each given sub-goal, the associated operator and operands are selected with statistical models. Lastly, it performs inference on logic expressions to get the answer and explains how the answer is obtained in a human comprehensible way. This process thus resembles the human cognitive understanding of the problem and produces a more meaningful problem solving interpretation.