Fluency Boost Learning and Inference for Neural Grammatical Error Correction
Tao Ge, Furu Wei, Ming Zhou · 2018
Most of the neural sequence-to-sequence (seq2seq) models for grammatical error correction (GEC) have two limitations: (1) a seq2seq model may not be well generalized with only limited error-corrected data; (2) a seq2seq model may fail to completely correct a sentence with multiple errors through normal seq2seq inference.We attempt to address these limitations by proposing a fluency boost learning and inference mechanism.Fluency boosting learning generates fluency-boost sentence pairs during training, enabling the error correction model to learn how to improve a sentence's fluency from more instances, while fluency boosting inference allows the model to correct a sentence incrementally through multi-round seq2seq inference until the sentence's fluency stops increasing.Experiments show our approaches improve the performance of seq2seq models for GEC, achieving state-of-the-art results on both CoNLL-2014 and JFLEG benchmark datasets.