2016 · 31 citations · 58 references
EducationMathematicsMathematics EducationAlgebra EducationMathematics InstructionNatural Language ProcessingMathematical WritingText GenerationHomework AssignmentsMathematics Word Problem InstructionNarrative RewritingProblem WritingStory EditingStory GenerationThematic CoherenceWord Problem Language
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.
58
George A. Miller · Communications of the ACM · 1995 · 14.1K citations · Full text
Efficient Estimation of Word Representations in Vector Space
Tomáš Mikolov, Kai Chen, Greg S. Corrado et al. · arXiv (Cornell University) · 2013 · 11.7K citations · Full text
The Stanford CoreNLP Natural Language Processing Toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer et al. · 2014 · 7.2K citations · Full text