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        <namePart type="given">Yumeng</namePart>
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    <abstract>For the grammatical error correction (GEC) task, there usually exist multiple correction ways for an erroneous input sentence, leading to multiple references. Observing the high proportion of multi-reference instances in Chinese GEC training data, we target a systematic study on how to better utilize multi-reference training data. We propose two new approaches and a simple two-stage training strategy. We compare them against previously proposed approaches, on two Chinese training datasets, i.e., Lang-8 for second language learner texts and FCGEC-Train for native speaker texts, and three test datasets. The experiments and analyses demonstrate the effectiveness of our proposed approaches and reveal interesting insights. Our code is available at https://github.com/ymliucs/MrGEC.</abstract>
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%0 Conference Proceedings
%T Towards Better Utilization of Multi-Reference Training Data for Chinese Grammatical Error Correction
%A Liu, Yumeng
%A Li, Zhenghua
%A Jiang, HaoChen
%A Zhang, Bo
%A Li, Chen
%A Zhang, Ji
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F liu-etal-2024-towards-better
%X For the grammatical error correction (GEC) task, there usually exist multiple correction ways for an erroneous input sentence, leading to multiple references. Observing the high proportion of multi-reference instances in Chinese GEC training data, we target a systematic study on how to better utilize multi-reference training data. We propose two new approaches and a simple two-stage training strategy. We compare them against previously proposed approaches, on two Chinese training datasets, i.e., Lang-8 for second language learner texts and FCGEC-Train for native speaker texts, and three test datasets. The experiments and analyses demonstrate the effectiveness of our proposed approaches and reveal interesting insights. Our code is available at https://github.com/ymliucs/MrGEC.
%U https://aclanthology.org/2024.findings-acl.180
%P 3044-3052

Markdown (Informal)

[Towards Better Utilization of Multi-Reference Training Data for Chinese Grammatical Error Correction](https://aclanthology.org/2024.findings-acl.180) (Liu et al., Findings 2024)

• Towards Better Utilization of Multi-Reference Training Data for Chinese Grammatical Error Correction (Liu et al., Findings 2024)

ACL

• Yumeng Liu, Zhenghua Li, HaoChen Jiang, Bo Zhang, Chen Li, and Ji Zhang. 2024. Towards Better Utilization of Multi-Reference Training Data for Chinese Grammatical Error Correction. In Findings of the Association for Computational Linguistics ACL 2024, pages 3044–3052, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.