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        <title>A Low-Resource Approach to the Grammatical Error Correction of Ukrainian</title>
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    <name type="personal">
        <namePart type="given">Frank</namePart>
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    <name type="personal">
        <namePart type="given">Alla</namePart>
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            <title>Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)</title>
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            <namePart type="given">Mariana</namePart>
            <namePart type="family">Romanyshyn</namePart>
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    <abstract>We present our system that participated in the shared task on the grammatical error correction of Ukrainian. We have implemented two approaches that make use of large pre-trained language models and synthetic data, that have been used for error correction of English as well as low-resource languages. The first approach is based on fine-tuning a large multilingual language model (mT5) in two stages: first, on synthetic data, and then on gold data. The second approach trains a (smaller) seq2seq Transformer model pre-trained on synthetic data and fine-tuned on gold data. Our mT5-based model scored first in “GEC only” track, and a very close second in the “GEC+Fluency” track. Our two key innovations are (1) finetuning in stages, first on synthetic, and then on gold data; and (2) a high-quality corruption method based on roundtrip machine translation to complement existing noisification approaches.</abstract>
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%0 Conference Proceedings
%T A Low-Resource Approach to the Grammatical Error Correction of Ukrainian
%A Palma Gomez, Frank
%A Rozovskaya, Alla
%A Roth, Dan
%Y Romanyshyn, Mariana
%S Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F gomez-etal-2023-low
%X We present our system that participated in the shared task on the grammatical error correction of Ukrainian. We have implemented two approaches that make use of large pre-trained language models and synthetic data, that have been used for error correction of English as well as low-resource languages. The first approach is based on fine-tuning a large multilingual language model (mT5) in two stages: first, on synthetic data, and then on gold data. The second approach trains a (smaller) seq2seq Transformer model pre-trained on synthetic data and fine-tuned on gold data. Our mT5-based model scored first in “GEC only” track, and a very close second in the “GEC+Fluency” track. Our two key innovations are (1) finetuning in stages, first on synthetic, and then on gold data; and (2) a high-quality corruption method based on roundtrip machine translation to complement existing noisification approaches.
%R 10.18653/v1/2023.unlp-1.14
%U https://aclanthology.org/2023.unlp-1.14
%U https://doi.org/10.18653/v1/2023.unlp-1.14
%P 114-120

Markdown (Informal)

[A Low-Resource Approach to the Grammatical Error Correction of Ukrainian](https://aclanthology.org/2023.unlp-1.14) (Palma Gomez et al., UNLP 2023)

• A Low-Resource Approach to the Grammatical Error Correction of Ukrainian (Palma Gomez et al., UNLP 2023)

ACL

• Frank Palma Gomez, Alla Rozovskaya, and Dan Roth. 2023. A Low-Resource Approach to the Grammatical Error Correction of Ukrainian. In Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP), pages 114–120, Dubrovnik, Croatia. Association for Computational Linguistics.