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        <title>Adversarial Grammatical Error Correction</title>
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            <namePart type="given">Trevor</namePart>
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    <abstract>Recent works in Grammatical Error Correction (GEC) have leveraged the progress in Neural Machine Translation (NMT), to learn rewrites from parallel corpora of grammatically incorrect and corrected sentences, achieving state-of-the-art results. At the same time, Generative Adversarial Networks (GANs) have been successful in generating realistic texts across many different tasks by learning to directly minimize the difference between human-generated and synthetic text. In this work, we present an adversarial learning approach to GEC, using the generator-discriminator framework. The generator is a Transformer model, trained to produce grammatically correct sentences given grammatically incorrect ones. The discriminator is a sentence-pair classification model, trained to judge a given pair of grammatically incorrect-correct sentences on the quality of grammatical correction. We pre-train both the discriminator and the generator on parallel texts and then fine-tune them further using a policy gradient method that assigns high rewards to sentences which could be true corrections of the grammatically incorrect text. Experimental results on FCE, CoNLL-14, and BEA-19 datasets show that Adversarial-GEC can achieve competitive GEC quality compared to NMT-based baselines.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.275</identifier>
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%0 Conference Proceedings
%T Adversarial Grammatical Error Correction
%A Raheja, Vipul
%A Alikaniotis, Dimitris
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F raheja-alikaniotis-2020-adversarial
%X Recent works in Grammatical Error Correction (GEC) have leveraged the progress in Neural Machine Translation (NMT), to learn rewrites from parallel corpora of grammatically incorrect and corrected sentences, achieving state-of-the-art results. At the same time, Generative Adversarial Networks (GANs) have been successful in generating realistic texts across many different tasks by learning to directly minimize the difference between human-generated and synthetic text. In this work, we present an adversarial learning approach to GEC, using the generator-discriminator framework. The generator is a Transformer model, trained to produce grammatically correct sentences given grammatically incorrect ones. The discriminator is a sentence-pair classification model, trained to judge a given pair of grammatically incorrect-correct sentences on the quality of grammatical correction. We pre-train both the discriminator and the generator on parallel texts and then fine-tune them further using a policy gradient method that assigns high rewards to sentences which could be true corrections of the grammatically incorrect text. Experimental results on FCE, CoNLL-14, and BEA-19 datasets show that Adversarial-GEC can achieve competitive GEC quality compared to NMT-based baselines.
%R 10.18653/v1/2020.findings-emnlp.275
%U https://aclanthology.org/2020.findings-emnlp.275/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.275
%P 3075-3087

Markdown (Informal)

[Adversarial Grammatical Error Correction](https://aclanthology.org/2020.findings-emnlp.275/) (Raheja & Alikaniotis, Findings 2020)

• Adversarial Grammatical Error Correction (Raheja & Alikaniotis, Findings 2020)

ACL

• Vipul Raheja and Dimitris Alikaniotis. 2020. Adversarial Grammatical Error Correction. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3075–3087, Online. Association for Computational Linguistics.