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        <title>Grammatical Error Correction with Contrastive Learning in Low Error Density Domains</title>
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    <name type="personal">
        <namePart type="given">Hannan</namePart>
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            <namePart type="given">Marie-Francine</namePart>
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            <namePart type="given">Xuanjing</namePart>
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            <namePart type="given">Lucia</namePart>
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    <abstract>Although grammatical error correction (GEC) has achieved good performance on texts written by learners of English as a second language, performance on low error density domains where texts are written by English speakers of varying levels of proficiency can still be improved. In this paper, we propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate, so as to improve the accuracy of the model. Experimental results show that our approach significantly improves the performance of GEC models in low error density domains, when evaluated on the benchmark CWEB dataset.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.419</identifier>
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%0 Conference Proceedings
%T Grammatical Error Correction with Contrastive Learning in Low Error Density Domains
%A Cao, Hannan
%A Yang, Wenmian
%A Ng, Hwee Tou
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F cao-etal-2021-grammatical-error
%X Although grammatical error correction (GEC) has achieved good performance on texts written by learners of English as a second language, performance on low error density domains where texts are written by English speakers of varying levels of proficiency can still be improved. In this paper, we propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate, so as to improve the accuracy of the model. Experimental results show that our approach significantly improves the performance of GEC models in low error density domains, when evaluated on the benchmark CWEB dataset.
%R 10.18653/v1/2021.findings-emnlp.419
%U https://aclanthology.org/2021.findings-emnlp.419/
%U https://doi.org/10.18653/v1/2021.findings-emnlp.419
%P 4867-4874

Markdown (Informal)

[Grammatical Error Correction with Contrastive Learning in Low Error Density Domains](https://aclanthology.org/2021.findings-emnlp.419/) (Cao et al., Findings 2021)

• Grammatical Error Correction with Contrastive Learning in Low Error Density Domains (Cao et al., Findings 2021)

ACL

• Hannan Cao, Wenmian Yang, and Hwee Tou Ng. 2021. Grammatical Error Correction with Contrastive Learning in Low Error Density Domains. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4867–4874, Punta Cana, Dominican Republic. Association for Computational Linguistics.