Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems
Ling Wang, Dani Yogatama, Chris Dyer, Phil Blunsom · 2017
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.