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JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
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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.
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Neural Grammatical Error Correction for Romanian
A new Romanian GEC corpus of 10k pairs plus pretraining a Transformer on artificial errors generated via POS tagger yields F0.5 of 53.76, beating the 44.38 baseline from training only on the corpus.
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