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        <title>Driving Chinese Spelling Correction from a Fine-Grained Perspective</title>
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            <title>Proceedings of the 31st International Conference on Computational Linguistics</title>
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    <abstract>This paper explores the task: Chinese spelling correction (CSC), from a fine-grained perspec- tive by recognizing that existing evaluations lack nuanced typology for the spelling errors. This deficiency can create a misleading impres- sion of model performance, incurring an “in- visible” bottleneck hindering the advancement of CSC research. In this paper, we first cate- gorize spelling errors into six types and con- duct a fine-grained evaluation across a wide variety of models, including BERT-based mod- els and LLMs. Thus, we are able to pinpoint the underlying weaknesses of existing state-of- the-art models - utilizing contextual clues and handling co-existence of multiple typos, asso- ciated to contextual errors and multi-typo er- rors. However, these errors occur infrequently in conventional training corpus. Therefore, we introduce new error generation methods to aug- ment their occurrence, which can be leveraged to enhance the training of CSC models. We hope this work could provide fresh insight for future CSC research.</abstract>
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%0 Conference Proceedings
%T Driving Chinese Spelling Correction from a Fine-Grained Perspective
%A Liu, Linfeng
%A Wu, Hongqiu
%A Zhao, Hai
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F liu-etal-2025-driving
%X This paper explores the task: Chinese spelling correction (CSC), from a fine-grained perspec- tive by recognizing that existing evaluations lack nuanced typology for the spelling errors. This deficiency can create a misleading impres- sion of model performance, incurring an “in- visible” bottleneck hindering the advancement of CSC research. In this paper, we first cate- gorize spelling errors into six types and con- duct a fine-grained evaluation across a wide variety of models, including BERT-based mod- els and LLMs. Thus, we are able to pinpoint the underlying weaknesses of existing state-of- the-art models - utilizing contextual clues and handling co-existence of multiple typos, asso- ciated to contextual errors and multi-typo er- rors. However, these errors occur infrequently in conventional training corpus. Therefore, we introduce new error generation methods to aug- ment their occurrence, which can be leveraged to enhance the training of CSC models. We hope this work could provide fresh insight for future CSC research.
%U https://aclanthology.org/2025.coling-main.715/
%P 10727-10737

Markdown (Informal)

[Driving Chinese Spelling Correction from a Fine-Grained Perspective](https://aclanthology.org/2025.coling-main.715/) (Liu et al., COLING 2025)

• Driving Chinese Spelling Correction from a Fine-Grained Perspective (Liu et al., COLING 2025)

ACL

• Linfeng Liu, Hongqiu Wu, and Hai Zhao. 2025. Driving Chinese Spelling Correction from a Fine-Grained Perspective. In Proceedings of the 31st International Conference on Computational Linguistics, pages 10727–10737, Abu Dhabi, UAE. Association for Computational Linguistics.