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        <title>Evolving Chinese Spelling Correction with Corrector-Verifier Collaboration</title>
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    <name type="personal">
        <namePart type="given">Linfeng</namePart>
        <namePart type="family">Liu</namePart>
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        <namePart type="given">Hongqiu</namePart>
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    <name type="personal">
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        <namePart type="family">Zhao</namePart>
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            <title>Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Christos</namePart>
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            <namePart type="given">Tanmoy</namePart>
            <namePart type="family">Chakraborty</namePart>
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    <abstract>Recent methods address Chinese Spelling Correction (CSC) with either BERT-based models or large language models (LLMs) independently. However, both of them face challenges. BERT-based models are efficient for this task but struggle with limited generalizability to error patterns, thus failing in open-domain CSC. LLMs are advantageous in their extensive knowledge but fall into low efficiency in character-level editing. To address this dilemma, we propose Automatic Corrector Iteration (ACI), a novel model collaboration pipeline to iteratively optimize a BERT-based corrector. This pipeline is free of human annotation, by leveraging an LLM verifier to provide useful signals for the corrector. Experimental results demonstrate that our pipeline consistently improves the model performance across iterations and significantly outperforms existing data augmentation methods, achieving comparable performance with human annotation.</abstract>
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        <date>2025-11</date>
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            <start>29010</start>
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%0 Conference Proceedings
%T Evolving Chinese Spelling Correction with Corrector-Verifier Collaboration
%A Liu, Linfeng
%A Wu, Hongqiu
%A Zhao, Hai
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F liu-etal-2025-evolving
%X Recent methods address Chinese Spelling Correction (CSC) with either BERT-based models or large language models (LLMs) independently. However, both of them face challenges. BERT-based models are efficient for this task but struggle with limited generalizability to error patterns, thus failing in open-domain CSC. LLMs are advantageous in their extensive knowledge but fall into low efficiency in character-level editing. To address this dilemma, we propose Automatic Corrector Iteration (ACI), a novel model collaboration pipeline to iteratively optimize a BERT-based corrector. This pipeline is free of human annotation, by leveraging an LLM verifier to provide useful signals for the corrector. Experimental results demonstrate that our pipeline consistently improves the model performance across iterations and significantly outperforms existing data augmentation methods, achieving comparable performance with human annotation.
%U https://aclanthology.org/2025.emnlp-main.1477/
%P 29010-29016

Markdown (Informal)

[Evolving Chinese Spelling Correction with Corrector-Verifier Collaboration](https://aclanthology.org/2025.emnlp-main.1477/) (Liu et al., EMNLP 2025)

• Evolving Chinese Spelling Correction with Corrector-Verifier Collaboration (Liu et al., EMNLP 2025)

ACL

• Linfeng Liu, Hongqiu Wu, and Hai Zhao. 2025. Evolving Chinese Spelling Correction with Corrector-Verifier Collaboration. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 29010–29016, Suzhou, China. Association for Computational Linguistics.