Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction

Dingyao Yu, Yang An, Wei Ye, Xiongfeng Xiao, Shaoguang Mao, Tao Ge, Shikun Zhang


Abstract
Chinese Spelling Correction (CSC) commonly lacks large-scale high-quality corpora, due to the labor-intensive labeling of spelling errors in real-life human writing or typing scenarios. Two data augmentation methods are widely adopted: (1) *Random Replacement* with the guidance of confusion sets and (2) *OCR/ASR-based Generation* that simulates character misusing. However, both methods inevitably introduce noisy data (e.g., false spelling errors), potentially leading to over-correction. By carefully analyzing the two types of corpora, we find that though the latter achieves more robust generalization performance, the former yields better-calibrated CSC models. We then provide a theoretical analysis of this empirical observation, based on which a corpus refining strategy is proposed. Specifically, OCR/ASR-based data samples are fed into a well-calibrated CSC model trained on random replacement-based corpora and then filtered based on prediction confidence. By learning a simple BERT-based model on the refined OCR/ASR-based corpus, we set up impressive state-of-the-art performance on three widely-used benchmarks, while significantly alleviating over-correction (e.g., lowering false positive predictions).
Anthology ID:
2024.findings-acl.914
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
15468–15480
Language:
URL:
https://aclanthology.org/2024.findings-acl.914
DOI:
Bibkey:
Cite (ACL):
Dingyao Yu, Yang An, Wei Ye, Xiongfeng Xiao, Shaoguang Mao, Tao Ge, and Shikun Zhang. 2024. Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction. In Findings of the Association for Computational Linguistics ACL 2024, pages 15468–15480, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction (Yu et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.914.pdf