Adapting Sequence Models for Sentence Correction
EMNLP, pp. 2807-2813, 2017.
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 ...More
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