Weakly Supervised Formula Learner for Solving Mathematical Problems

Yuxuan Wu, Hideki Nakayama


Abstract
Mathematical reasoning task is a subset of the natural language question answering task. Existing work suggested solving this task with a two-phase approach, where the model first predicts formulas from questions and then calculates answers from such formulas. This approach achieved desirable performance in existing work. However, its reliance on annotated formulas as intermediate labels throughout its training limited its application. In this work, we put forward the idea to enable models to learn optimal formulas autonomously. We proposed Weakly Supervised Formula Learner, a learning framework that drives the formula exploration with weak supervision from the final answers to mathematical problems. Our experiments are conducted on two representative mathematical reasoning datasets MathQA and Math23K. On MathQA, our method outperformed baselines trained on complete yet imperfect formula annotations. On Math23K, our method outperformed other weakly supervised learning methods.
Anthology ID:
2022.coling-1.150
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1743–1752
Language:
URL:
https://aclanthology.org/2022.coling-1.150
DOI:
Bibkey:
Cite (ACL):
Yuxuan Wu and Hideki Nakayama. 2022. Weakly Supervised Formula Learner for Solving Mathematical Problems. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1743–1752, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Weakly Supervised Formula Learner for Solving Mathematical Problems (Wu & Nakayama, COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.150.pdf
Code
 evan-ak/wsfl
Data
Math23KMathQA