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        <title>Fluency Boost Learning and Inference for Neural Grammatical Error Correction</title>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>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 with multiple inference steps 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.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1097</identifier>
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%0 Conference Proceedings
%T Fluency Boost Learning and Inference for Neural Grammatical Error Correction
%A Ge, Tao
%A Wei, Furu
%A Zhou, Ming
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F ge-etal-2018-fluency
%X 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 with multiple inference steps 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.
%R 10.18653/v1/P18-1097
%U https://aclanthology.org/P18-1097
%U https://doi.org/10.18653/v1/P18-1097
%P 1055-1065

Markdown (Informal)

[Fluency Boost Learning and Inference for Neural Grammatical Error Correction](https://aclanthology.org/P18-1097) (Ge et al., ACL 2018)

• Fluency Boost Learning and Inference for Neural Grammatical Error Correction (Ge et al., ACL 2018)

ACL

• Tao Ge, Furu Wei, and Ming Zhou. 2018. Fluency Boost Learning and Inference for Neural Grammatical Error Correction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1055–1065, Melbourne, Australia. Association for Computational Linguistics.