Fluency Boost Learning and Inference for Neural Grammatical Error Correction

@inproceedings{Ge2018FluencyBL,
  title={Fluency Boost Learning and Inference for Neural Grammatical Error Correction},
  author={Tao Ge and Furu Wei and M. Zhou},
  booktitle={ACL},
  year={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. [...] Key Method 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…Expand
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