708 research outputs found
Adapting Sequence Models for Sentence Correction
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.Comment: EMNLP 201
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
A Nested Attention Neural Hybrid Model for Grammatical Error Correction
Grammatical error correction (GEC) systems strive to correct both global
errors in word order and usage, and local errors in spelling and inflection.
Further developing upon recent work on neural machine translation, we propose a
new hybrid neural model with nested attention layers for GEC. Experiments show
that the new model can effectively correct errors of both types by
incorporating word and character-level information,and that the model
significantly outperforms previous neural models for GEC as measured on the
standard CoNLL-14 benchmark dataset. Further analysis also shows that the
superiority of the proposed model can be largely attributed to the use of the
nested attention mechanism, which has proven particularly effective in
correcting local errors that involve small edits in orthography
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