Math Word Problem Solving by Generating Linguistic Variants of Problem Statements
Syed Rifat Raiyan, Md Nafis Faiyaz, Shah Md. Jawad Kabir, Mohsinul Kabir, Hasan Mahmud, Md Kamrul Hasan
Abstract
The art of mathematical reasoning stands as a fundamental pillar of intellectual progress and is a central catalyst in cultivating human ingenuity. Researchers have recently published a plethora of works centered around the task of solving Math Word Problems (MWP) — a crucial stride towards general AI. These existing models are susceptible to dependency on shallow heuristics and spurious correlations to derive the solution expressions. In order to ameliorate this issue, in this paper, we propose a framework for MWP solvers based on the generation of linguistic variants of the problem text. The approach involves solving each of the variant problems and electing the predicted expression with the majority of the votes. We use DeBERTa (Decoding-enhanced BERT with disentangled attention) as the encoder to leverage its rich textual representations and enhanced mask decoder to construct the solution expressions. Furthermore, we introduce a challenging dataset, ParaMAWPS, consisting of paraphrased, adversarial, and inverse variants of selectively sampled MWPs from the benchmark Mawps dataset. We extensively experiment on this dataset along with other benchmark datasets using some baseline MWP solver models. We show that training on linguistic variants of problem statements and voting on candidate predictions improve the mathematical reasoning and robustness of the model. We make our code and data publicly available.- Anthology ID:
- 2023.acl-srw.49
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Vishakh Padmakumar, Gisela Vallejo, Yao Fu
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 362–378
- Language:
- URL:
- https://aclanthology.org/2023.acl-srw.49
- DOI:
- 10.18653/v1/2023.acl-srw.49
- Cite (ACL):
- Syed Rifat Raiyan, Md Nafis Faiyaz, Shah Md. Jawad Kabir, Mohsinul Kabir, Hasan Mahmud, and Md Kamrul Hasan. 2023. Math Word Problem Solving by Generating Linguistic Variants of Problem Statements. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 362–378, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Math Word Problem Solving by Generating Linguistic Variants of Problem Statements (Raiyan et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.acl-srw.49.pdf