Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems
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.
Cite
@article{arxiv.1705.04146,
title = {Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems},
author = {Wang Ling and Dani Yogatama and Chris Dyer and Phil Blunsom},
journal= {arXiv preprint arXiv:1705.04146},
year = {2017}
}