Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving
Shih-hung
Tsai
author
Chao-Chun
Liang
author
Hsin-Min
Wang
author
Keh-Yih
Su
author
2021-08
text
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Chengqing
Zong
editor
Fei
Xia
editor
Wenjie
Li
editor
Roberto
Navigli
editor
Association for Computational Linguistics
Online
conference publication
With the recent advancements in deep learning, neural solvers have gained promising results in solving math word problems. However, these SOTA solvers only generate binary expression trees that contain basic arithmetic operators and do not explicitly use the math formulas. As a result, the expression trees they produce are lengthy and uninterpretable because they need to use multiple operators and constants to represent one single formula. In this paper, we propose sequence-to-general tree (S2G) that learns to generate interpretable and executable operation trees where the nodes can be formulas with an arbitrary number of arguments. With nodes now allowed to be formulas, S2G can learn to incorporate mathematical domain knowledge into problem-solving, making the results more interpretable. Experiments show that S2G can achieve a better performance against strong baselines on problems that require domain knowledge.
tsai-etal-2021-sequence
10.18653/v1/2021.acl-short.121
https://aclanthology.org/2021.acl-short.121
2021-08
964
972