Abstract

We present an approach for automatically learning to solve algebra word problems. Our algorithm reasons across sentence boundaries to construct and solve a sys-tem of linear equations, while simultane-ously recovering an alignment of the vari-ables and numbers in these equations to the problem text. The learning algorithm uses varied supervision, including either full equations or just the final answers. We evaluate performance on a newly gathered corpus of algebra word problems, demon-strating that the system can correctly an-swer almost 70 % of the questions in the dataset. This is, to our knowledge, the first learning result for this task. 1

Keywords

Computer scienceWord (group theory)Algebra over a fieldWord problem (mathematics education)Artificial intelligenceNatural language processingArithmeticLinguisticsMathematicsPure mathematics

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Publication Info

Year
2014
Type
article
Citations
356
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Closed

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Cite This

Nate Kushman, Yoav Artzi, Luke Zettlemoyer et al. (2014). Learning to Automatically Solve Algebra Word Problems. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . https://doi.org/10.3115/v1/p14-1026

Identifiers

DOI
10.3115/v1/p14-1026

Data Quality

Data completeness: 81%