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        <title>No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models</title>
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        <namePart type="given">Elizaveta</namePart>
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            <title>Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Yvette</namePart>
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            <namePart type="given">Matthew</namePart>
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    <abstract>Grammatical Error Correction (GEC) enhances language proficiency and promotes effective communication, but research has primarily centered around English. We propose a simple approach to multilingual and low-resource GEC by exploring the potential of multilingual machine translation (MT) models for error correction. We show that MT models are not only capable of error correction out-of-the-box, but that they can also be fine-tuned to even better correction quality. Results show the effectiveness of this approach, with our multilingual model outperforming similar-sized mT5-based models and even competing favourably with larger models.</abstract>
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%0 Conference Proceedings
%T No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models
%A Luhtaru, Agnes
%A Korotkova, Elizaveta
%A Fishel, Mark
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F luhtaru-etal-2024-error
%X Grammatical Error Correction (GEC) enhances language proficiency and promotes effective communication, but research has primarily centered around English. We propose a simple approach to multilingual and low-resource GEC by exploring the potential of multilingual machine translation (MT) models for error correction. We show that MT models are not only capable of error correction out-of-the-box, but that they can also be fine-tuned to even better correction quality. Results show the effectiveness of this approach, with our multilingual model outperforming similar-sized mT5-based models and even competing favourably with larger models.
%U https://aclanthology.org/2024.eacl-long.73
%P 1209-1222

Markdown (Informal)

[No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models](https://aclanthology.org/2024.eacl-long.73) (Luhtaru et al., EACL 2024)

• No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models (Luhtaru et al., EACL 2024)

ACL

• Agnes Luhtaru, Elizaveta Korotkova, and Mark Fishel. 2024. No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1209–1222, St. Julian’s, Malta. Association for Computational Linguistics.