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        <title>Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data</title>
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    <abstract>Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for the GEC task by copying the unchanged words from the source sentence to the target sentence. Since the GEC suffers from not having enough labeled training data to achieve high accuracy. We pre-train the copy-augmented architecture with a denoising auto-encoder using the unlabeled One Billion Benchmark and make comparisons between the fully pre-trained model and a partially pre-trained model. It is the first time copying words from the source context and fully pre-training a sequence to sequence model are experimented on the GEC task. Moreover, We add token-level and sentence-level multi-task learning for the GEC task. The evaluation results on the CoNLL-2014 test set show that our approach outperforms all recently published state-of-the-art results by a large margin.</abstract>
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%0 Conference Proceedings
%T Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data
%A Zhao, Wei
%A Wang, Liang
%A Shen, Kewei
%A Jia, Ruoyu
%A Liu, Jingming
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F zhao-etal-2019-improving
%X Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for the GEC task by copying the unchanged words from the source sentence to the target sentence. Since the GEC suffers from not having enough labeled training data to achieve high accuracy. We pre-train the copy-augmented architecture with a denoising auto-encoder using the unlabeled One Billion Benchmark and make comparisons between the fully pre-trained model and a partially pre-trained model. It is the first time copying words from the source context and fully pre-training a sequence to sequence model are experimented on the GEC task. Moreover, We add token-level and sentence-level multi-task learning for the GEC task. The evaluation results on the CoNLL-2014 test set show that our approach outperforms all recently published state-of-the-art results by a large margin.
%R 10.18653/v1/N19-1014
%U https://aclanthology.org/N19-1014
%U https://doi.org/10.18653/v1/N19-1014
%P 156-165

Markdown (Informal)

[Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data](https://aclanthology.org/N19-1014) (Zhao et al., NAACL 2019)

• Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data (Zhao et al., NAACL 2019)

ACL

• Wei Zhao, Liang Wang, Kewei Shen, Ruoyu Jia, and Jingming Liu. 2019. Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 156–165, Minneapolis, Minnesota. Association for Computational Linguistics.