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        <title>Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study</title>
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    <abstract>Sequence-to-sequence (seq2seq) models have achieved tremendous success in text generation tasks. However, there is no guarantee that they can always generate sentences without grammatical errors. In this paper, we present a preliminary empirical study on whether and how much automatic grammatical error correction can help improve seq2seq text generation. We conduct experiments across various seq2seq text generation tasks including machine translation, formality style transfer, sentence compression and simplification. Experiments show the state-of-the-art grammatical error correction system can improve the grammaticality of generated text and can bring task-oriented improvements in the tasks where target sentences are in a formal style.</abstract>
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%0 Conference Proceedings
%T Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study
%A Ge, Tao
%A Zhang, Xingxing
%A Wei, Furu
%A Zhou, Ming
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F ge-etal-2019-automatic
%X Sequence-to-sequence (seq2seq) models have achieved tremendous success in text generation tasks. However, there is no guarantee that they can always generate sentences without grammatical errors. In this paper, we present a preliminary empirical study on whether and how much automatic grammatical error correction can help improve seq2seq text generation. We conduct experiments across various seq2seq text generation tasks including machine translation, formality style transfer, sentence compression and simplification. Experiments show the state-of-the-art grammatical error correction system can improve the grammaticality of generated text and can bring task-oriented improvements in the tasks where target sentences are in a formal style.
%R 10.18653/v1/P19-1609
%U https://aclanthology.org/P19-1609
%U https://doi.org/10.18653/v1/P19-1609
%P 6059-6064

Markdown (Informal)

[Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study](https://aclanthology.org/P19-1609) (Ge et al., ACL 2019)

• Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study (Ge et al., ACL 2019)

ACL

• Tao Ge, Xingxing Zhang, Furu Wei, and Ming Zhou. 2019. Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6059–6064, Florence, Italy. Association for Computational Linguistics.