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arXiv · 1707.09067

Adapting Sequence Models for Sentence Correction

Allen Schmaltz·Yoon Kim·Alexander M. Rush·Stuart M. Shieber
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Abstract

In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches. Our strongest sequence-to-sequence model improves over our strongest phrase-based statistical machine translation model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally, in the data environment of the standard CoNLL-2014 setup, we demonstrate that modeling (and tuning against) diffs yields similar or better M2 scores with simpler models and/or significantly less data than previous sequence-to-sequence approaches.

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Allen Schmaltz, Yoon Kim, Alexander M. Rush, Stuart M. Shieber. 2017-07-27. Adapting Sequence Models for Sentence Correction. https://arxiv.org/abs/1707.09067

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