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        <title>Detection-Correction Structure via General Language Model for Grammatical Error Correction</title>
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            <namePart type="given">Lun-Wei</namePart>
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    <abstract>Grammatical error correction (GEC) is a task dedicated to rectifying texts with minimal edits, which can be decoupled into two components: detection and correction. However, previous works have predominantly focused on direct correction, with no prior efforts to integrate both into a single model. Moreover, the exploration of the detection-correction paradigm by large language models (LLMs) remains underdeveloped. This paper introduces an integrated detection-correction structure, named DeCoGLM, based on the General Language Model (GLM). The detection phase employs a fault-tolerant detection template, while the correction phase leverages autoregressive mask infilling for localized error correction. Through the strategic organization of input tokens and modification of attention masks, we facilitate multi-task learning within a single model. Our model demonstrates competitive performance against the state-of-the-art models on English and Chinese GEC datasets. Further experiments present the effectiveness of the detection-correction structure in LLMs, suggesting a promising direction for GEC.</abstract>
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%0 Conference Proceedings
%T Detection-Correction Structure via General Language Model for Grammatical Error Correction
%A Li, Wei
%A Wang, Houfeng
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F li-wang-2024-detection
%X Grammatical error correction (GEC) is a task dedicated to rectifying texts with minimal edits, which can be decoupled into two components: detection and correction. However, previous works have predominantly focused on direct correction, with no prior efforts to integrate both into a single model. Moreover, the exploration of the detection-correction paradigm by large language models (LLMs) remains underdeveloped. This paper introduces an integrated detection-correction structure, named DeCoGLM, based on the General Language Model (GLM). The detection phase employs a fault-tolerant detection template, while the correction phase leverages autoregressive mask infilling for localized error correction. Through the strategic organization of input tokens and modification of attention masks, we facilitate multi-task learning within a single model. Our model demonstrates competitive performance against the state-of-the-art models on English and Chinese GEC datasets. Further experiments present the effectiveness of the detection-correction structure in LLMs, suggesting a promising direction for GEC.
%U https://aclanthology.org/2024.acl-long.96
%P 1748-1763

Markdown (Informal)

[Detection-Correction Structure via General Language Model for Grammatical Error Correction](https://aclanthology.org/2024.acl-long.96) (Li & Wang, ACL 2024)

• Detection-Correction Structure via General Language Model for Grammatical Error Correction (Li & Wang, ACL 2024)

ACL

• Wei Li and Houfeng Wang. 2024. Detection-Correction Structure via General Language Model for Grammatical Error Correction. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1748–1763, Bangkok, Thailand. Association for Computational Linguistics.