Mining Commonsense and Domain Knowledge from Math Word Problems

Shih-Hung Tsai, Chao-Chun Liang, Hsin-Min Wang, Keh-Yih Su


Abstract
Current neural math solvers learn to incorporate commonsense or domain knowledge by utilizing pre-specified constants or formulas. However, as these constants and formulas are mainly human-specified, the generalizability of the solvers is limited. In this paper, we propose to explicitly retrieve the required knowledge from math problemdatasets. In this way, we can determinedly characterize the required knowledge andimprove the explainability of solvers. Our two algorithms take the problem text andthe solution equations as input. Then, they try to deduce the required commonsense and domain knowledge by integrating information from both parts. We construct two math datasets and show the effectiveness of our algorithms that they can retrieve the required knowledge for problem-solving.
Anthology ID:
2021.rocling-1.15
Volume:
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
Month:
October
Year:
2021
Address:
Taoyuan, Taiwan
Editors:
Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
Venue:
ROCLING
SIG:
Publisher:
The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Note:
Pages:
111–117
Language:
URL:
https://aclanthology.org/2021.rocling-1.15
DOI:
Bibkey:
Cite (ACL):
Shih-Hung Tsai, Chao-Chun Liang, Hsin-Min Wang, and Keh-Yih Su. 2021. Mining Commonsense and Domain Knowledge from Math Word Problems. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 111–117, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).
Cite (Informal):
Mining Commonsense and Domain Knowledge from Math Word Problems (Tsai et al., ROCLING 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.rocling-1.15.pdf