Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems
Wang
Ling
author
Dani
Yogatama
author
Chris
Dyer
author
Phil
Blunsom
author
2017-07
text
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Regina
Barzilay
editor
Min-Yen
Kan
editor
Association for Computational Linguistics
Vancouver, Canada
conference publication
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.
ling-etal-2017-program
10.18653/v1/P17-1015
https://aclanthology.org/P17-1015
2017-07
158
167