Abstract
Solving algebraic word problems requires executing a series of arithmetic operations—a program—to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.- Anthology ID:
- P17-1015
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 158–167
- Language:
- URL:
- https://aclanthology.org/P17-1015
- DOI:
- 10.18653/v1/P17-1015
- Cite (ACL):
- Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017. Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 158–167, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems (Ling et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/P17-1015.pdf