Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems

作者：Wang Ling, Dani Yogatama, Chris Dyer, P. Blunsom · 发表于：Annual Meeting of the Association for Computational Linguistics · 年份：2017 · DOI：10.18653/v1/p17-1015 · 被引用次数：1053 · 研究领域：Computer Science

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.