A Theme-Rewriting Approach for Generating Algebra Word Problems

Rik Koncel-Kedziorski, Ioannis Konstas, Luke Zettlemoyer, Hannaneh Hajishirzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western. In this paper, we present a text generation method called rewriting that edits existing human-authored narratives to change their theme without changing the underlying story. We apply the approach to math word problems, where it might help students stay more engaged by quickly transforming all of their homework assignments to the theme of their favorite movie without changing the math concepts that are being taught. Our rewriting method uses a twostage decoding process, which proposes new words from the target theme and scores the resulting stories according to a number of factors defining aspects of syntactic, semantic, and thematic coherence. Experiments demonstrate that the final stories typically represent the new theme well while still testing the original math concepts, outperforming a number of baselines. We also release a new dataset of human-authored rewrites of math word problems in several themes.
Original languageEnglish
Title of host publicationProceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics
Pages1617–1628
Number of pages12
ISBN (Print)9781945626258
Publication statusPublished - 1 Nov 2016
Event2016 Conference on Empirical Methods in Natural Language Processing - Austin, United States
Duration: 1 Nov 20165 Nov 2016

Conference

Conference2016 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2016
CountryUnited States
CityAustin
Period1/11/165/11/16

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  • Cite this

    Koncel-Kedziorski, R., Konstas, I., Zettlemoyer, L., & Hajishirzi, H. (2016). A Theme-Rewriting Approach for Generating Algebra Word Problems. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 1617–1628). Association for Computational Linguistics. https://www.aclweb.org/anthology/D16-1168