@inproceedings{wei-etal-2024-training,
title = "Training a Better {C}hinese Spelling Correction Model via Prior-knowledge Guided Teacher",
author = "Wei, Chi and
Huang, Shaobin and
Li, Rongsheng and
Yan, Naiyu and
Wang, Rui",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.806/",
doi = "10.18653/v1/2024.findings-acl.806",
pages = "13578--13589",
abstract = "Recent advancements in Chinese Spelling Correction (CSC) predominantly leverage pre-trained language models (PLMs). However, a notable challenge with fine-tuned PLM-based CSC models is their tendency to over-correct, leading to poor generalization for error patterns outside the standard distribution. To address this, we developed a teacher network guided by prior knowledge for distillation learning of CSC models. Unlike traditional teacher networks, which depend on task-related pre-training, our method infuses task-related prior information into the teacher network, offering guidance beyond mere labels to the student network. This strategy significantly enhances the CSC model`s language modeling capabilities, crucial for minimizing over-correction. Importantly, our approach is model-independent and the teacher network does not require task-related pre-training, making it broadly applicable for enhancing various PLM-based CSC models with minimal additional computational resources. Extensive experiments on widely used benchmarks demonstrate that our method achieves new state-of-the-art results. Additionally, we explored the potential of generalizing our method to other non-autoregressive text-generation tasks."
}
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<abstract>Recent advancements in Chinese Spelling Correction (CSC) predominantly leverage pre-trained language models (PLMs). However, a notable challenge with fine-tuned PLM-based CSC models is their tendency to over-correct, leading to poor generalization for error patterns outside the standard distribution. To address this, we developed a teacher network guided by prior knowledge for distillation learning of CSC models. Unlike traditional teacher networks, which depend on task-related pre-training, our method infuses task-related prior information into the teacher network, offering guidance beyond mere labels to the student network. This strategy significantly enhances the CSC model‘s language modeling capabilities, crucial for minimizing over-correction. Importantly, our approach is model-independent and the teacher network does not require task-related pre-training, making it broadly applicable for enhancing various PLM-based CSC models with minimal additional computational resources. Extensive experiments on widely used benchmarks demonstrate that our method achieves new state-of-the-art results. Additionally, we explored the potential of generalizing our method to other non-autoregressive text-generation tasks.</abstract>
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%0 Conference Proceedings
%T Training a Better Chinese Spelling Correction Model via Prior-knowledge Guided Teacher
%A Wei, Chi
%A Huang, Shaobin
%A Li, Rongsheng
%A Yan, Naiyu
%A Wang, Rui
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wei-etal-2024-training
%X Recent advancements in Chinese Spelling Correction (CSC) predominantly leverage pre-trained language models (PLMs). However, a notable challenge with fine-tuned PLM-based CSC models is their tendency to over-correct, leading to poor generalization for error patterns outside the standard distribution. To address this, we developed a teacher network guided by prior knowledge for distillation learning of CSC models. Unlike traditional teacher networks, which depend on task-related pre-training, our method infuses task-related prior information into the teacher network, offering guidance beyond mere labels to the student network. This strategy significantly enhances the CSC model‘s language modeling capabilities, crucial for minimizing over-correction. Importantly, our approach is model-independent and the teacher network does not require task-related pre-training, making it broadly applicable for enhancing various PLM-based CSC models with minimal additional computational resources. Extensive experiments on widely used benchmarks demonstrate that our method achieves new state-of-the-art results. Additionally, we explored the potential of generalizing our method to other non-autoregressive text-generation tasks.
%R 10.18653/v1/2024.findings-acl.806
%U https://aclanthology.org/2024.findings-acl.806/
%U https://doi.org/10.18653/v1/2024.findings-acl.806
%P 13578-13589
Markdown (Informal)
[Training a Better Chinese Spelling Correction Model via Prior-knowledge Guided Teacher](https://aclanthology.org/2024.findings-acl.806/) (Wei et al., Findings 2024)
- Training a Better Chinese Spelling Correction Model via Prior-knowledge Guided Teacher (Wei et al., Findings 2024)
ACL
- Chi Wei, Shaobin Huang, Rongsheng Li, Naiyu Yan, and Rui Wang. 2024. Training a Better Chinese Spelling Correction Model via Prior-knowledge Guided Teacher. In Findings of the Association for Computational Linguistics: ACL 2024, pages 13578–13589, Bangkok, Thailand. Association for Computational Linguistics.