@inproceedings{tsai-etal-2021-mining,
title = "Mining Commonsense and Domain Knowledge from Math Word Problems",
author = "Tsai, Shih-Hung and
Liang, Chao-Chun and
Wang, Hsin-Min and
Su, Keh-Yih",
booktitle = "Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)",
month = oct,
year = "2021",
address = "Taoyuan, Taiwan",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
url = "https://aclanthology.org/2021.rocling-1.15",
pages = "111--117",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Mining Commonsense and Domain Knowledge from Math Word Problems
%A Tsai, Shih-Hung
%A Liang, Chao-Chun
%A Wang, Hsin-Min
%A Su, Keh-Yih
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 oct
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F tsai-etal-2021-mining
%X 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.
%U https://aclanthology.org/2021.rocling-1.15
%P 111-117
Markdown (Informal)
[Mining Commonsense and Domain Knowledge from Math Word Problems](https://aclanthology.org/2021.rocling-1.15) (Tsai et al., ROCLING 2021)
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).