Papers with distillation learning of

1 papers
Training a Better Chinese Spelling Correction Model via Prior-knowledge Guided Teacher (2024.findings-acl)

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Challenge: Chinese Spelling Correction models are prone to over-correct and poor generalization for error patterns outside the standard distribution.
Approach: They propose a teacher network guided by prior knowledge for distillation learning of CSC models.
Outcome: The proposed method significantly enhances the CSC model’s language modeling capabilities, crucial for minimizing over-correction.

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