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    <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.</abstract>
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%0 Conference Proceedings
%T Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems
%A Ling, Wang
%A Yogatama, Dani
%A Dyer, Chris
%A Blunsom, Phil
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F ling-etal-2017-program
%X 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.
%R 10.18653/v1/P17-1015
%U https://aclanthology.org/P17-1015
%U https://doi.org/10.18653/v1/P17-1015
%P 158-167

Markdown (Informal)

[Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems](https://aclanthology.org/P17-1015) (Ling et al., ACL 2017)

• Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems (Ling et al., ACL 2017)

ACL

• Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017. Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 158–167, Vancouver, Canada. Association for Computational Linguistics.