A Small and Fast BERT for Chinese Medical Punctuation Restoration
This addresses the need for fast and accurate punctuation restoration in clinical dictation, though it is incremental as it builds on existing pre-trained models.
The authors tackled the problem of automatic punctuation restoration in Chinese medical dictation to prevent misunderstandings in clinical reports, achieving 95% performance with only 10% model size compared to state-of-the-art models.
In clinical dictation, utterances after automatic speech recognition (ASR) without explicit punctuation marks may lead to the misunderstanding of dictated reports. To give a precise and understandable clinical report with ASR, automatic punctuation restoration is required. Considering a practical scenario, we propose a fast and light pre-trained model for Chinese medical punctuation restoration based on 'pretraining and fine-tuning' paradigm. In this work, we distill pre-trained models by incorporating supervised contrastive learning and a novel auxiliary pre-training task (Punctuation Mark Prediction) to make it well-suited for punctuation restoration. Our experiments on various distilled models reveal that our model can achieve 95% performance while 10% model size relative to state-of-the-art Chinese RoBERTa.