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We present a novel evaluation method for grammatical error correction that addresses problems with previous approaches and scores systems in terms of improvement on the original text. Our method evaluates corrections at the token level using a globally optimal alignment between the source, a system hypothesis, and a reference. Unlike the M 2 Scorer, our method provides scores for both detection and correction and is sensitive to different types of edit operations.
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Felice et al. (2015) studied this question.