Rethinking Masked Language Modeling for Chinese Spelling Correction

• URL: http://arxiv.org/abs/2305.17721v1
• Date: Sun, 28 May 2023 13:19:12 GMT
• Title: Rethinking Masked Language Modeling for Chinese Spelling Correction
• Authors: Hongqiu Wu and Shaohua Zhang and Yuchen Zhang and Hai Zhao
• Abstract summary: We study Chinese Spelling Correction (CSC) as a joint decision made by two separate models: a language model and an error model. We find that fine-tuning BERT tends to over-fit the error model while under-fit the language model, resulting in poor generalization to out-of-distribution error patterns. We demonstrate that a very simple strategy, randomly masking 20% non-error tokens from the input sequence during fine-tuning is sufficient for learning a much better language model without sacrificing the error model.
• Score: 70.85829000570203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study Chinese Spelling Correction (CSC) as a joint decision made by two separate models: a language model and an error model. Through empirical analysis, we find that fine-tuning BERT tends to over-fit the error model while under-fit the language model, resulting in poor generalization to out-of-distribution error patterns. Given that BERT is the backbone of most CSC models, this phenomenon has a significant negative impact. To address this issue, we are releasing a multi-domain benchmark LEMON, with higher quality and diversity than existing benchmarks, to allow a comprehensive assessment of the open domain generalization of CSC models. Then, we demonstrate that a very simple strategy, randomly masking 20\% non-error tokens from the input sequence during fine-tuning is sufficient for learning a much better language model without sacrificing the error model. This technique can be applied to any model architecture and achieves new state-of-the-art results on SIGHAN, ECSpell, and LEMON.

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