91 research outputs found
JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for
developing and evaluating grammatical error correction (GEC). Unlike other
corpora, it represents a broad range of language proficiency levels and uses
holistic fluency edits to not only correct grammatical errors but also make the
original text more native sounding. We describe the types of corrections made
and benchmark four leading GEC systems on this corpus, identifying specific
areas in which they do well and how they can improve. JFLEG fulfills the need
for a new gold standard to properly assess the current state of GEC.Comment: To appear in EACL 2017 (short papers
Using parse features for preposition selection and error detection
We evaluate the effect of adding parse features to a leading model of preposition usage. Results show a significant improvement in the preposition selection task on
native speaker text and a modest increment in precision and recall in an ESL error detection task. Analysis of the parser output indicates that it is robust enough in the face
of noisy non-native writing to extract useful information
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