“Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems”, 2017-05-11 (; similar):
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.