@inproceedings{qiao-etal-2025-disc,
title = "{DISC}: Plug-and-Play Decoding Intervention with Similarity of Characters for {C}hinese Spelling Check",
author = "Qiao, Ziheng and
Zhou, Houquan and
Liu, Yumeng and
Li, Zhenghua and
Zhang, Min and
Zhang, Bo and
Li, Chen and
Zhang, Ji and
Huang, Fei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1373/",
doi = "10.18653/v1/2025.acl-long.1373",
pages = "28312--28324",
ISBN = "979-8-89176-251-0",
abstract = "One key characteristic of the Chinese spelling check (CSC) task is that incorrect characters are usually similar to the correct ones in either phonetics or glyph. To accommodate this, previous works usually leverage confusion sets, which suffer from two problems, i.e., difficulty in determining which character pairs to include and lack of probabilities to distinguish items in the set. In this paper, we propose a light-weight plug-and-play DISC (i.e., decoding intervention with similarity of characters) module for CSC models. DISC measures phonetic and glyph similarities between characters and incorporates this similarity information only during the inference phase. This method can be easily integrated into various existing CSC models, such as ReaLiSe, SCOPE, and ReLM, without additional training costs. Experiments on three CSC benchmarks demonstrate that our proposed method significantly improves model performance, approaching and even surpassing the current state-of-the-art models."
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<abstract>One key characteristic of the Chinese spelling check (CSC) task is that incorrect characters are usually similar to the correct ones in either phonetics or glyph. To accommodate this, previous works usually leverage confusion sets, which suffer from two problems, i.e., difficulty in determining which character pairs to include and lack of probabilities to distinguish items in the set. In this paper, we propose a light-weight plug-and-play DISC (i.e., decoding intervention with similarity of characters) module for CSC models. DISC measures phonetic and glyph similarities between characters and incorporates this similarity information only during the inference phase. This method can be easily integrated into various existing CSC models, such as ReaLiSe, SCOPE, and ReLM, without additional training costs. Experiments on three CSC benchmarks demonstrate that our proposed method significantly improves model performance, approaching and even surpassing the current state-of-the-art models.</abstract>
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%0 Conference Proceedings
%T DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check
%A Qiao, Ziheng
%A Zhou, Houquan
%A Liu, Yumeng
%A Li, Zhenghua
%A Zhang, Min
%A Zhang, Bo
%A Li, Chen
%A Zhang, Ji
%A Huang, Fei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F qiao-etal-2025-disc
%X One key characteristic of the Chinese spelling check (CSC) task is that incorrect characters are usually similar to the correct ones in either phonetics or glyph. To accommodate this, previous works usually leverage confusion sets, which suffer from two problems, i.e., difficulty in determining which character pairs to include and lack of probabilities to distinguish items in the set. In this paper, we propose a light-weight plug-and-play DISC (i.e., decoding intervention with similarity of characters) module for CSC models. DISC measures phonetic and glyph similarities between characters and incorporates this similarity information only during the inference phase. This method can be easily integrated into various existing CSC models, such as ReaLiSe, SCOPE, and ReLM, without additional training costs. Experiments on three CSC benchmarks demonstrate that our proposed method significantly improves model performance, approaching and even surpassing the current state-of-the-art models.
%R 10.18653/v1/2025.acl-long.1373
%U https://aclanthology.org/2025.acl-long.1373/
%U https://doi.org/10.18653/v1/2025.acl-long.1373
%P 28312-28324
Markdown (Informal)
[DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check](https://aclanthology.org/2025.acl-long.1373/) (Qiao et al., ACL 2025)
ACL
- Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, and Fei Huang. 2025. DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 28312–28324, Vienna, Austria. Association for Computational Linguistics.