KNOWLEDGE DISTILLATION FROM BERT IN PRE-TRAINING AND FINE-TUNING FOR POLYPHONE DISAMBIGUATION

Polyphone disambiguation aims to select the correct pronunciation for a polyphonic word from several candidates, which is important for text-to-speech synthesis. Since the pronunciation of a polyphonic word is usually decided by its context, polyphone disambiguation can be regarded as a language understanding task. Inspired by the success of BERT for language understanding, we propose to leverage pre-trained BERT models for polyphone disambiguation. However, BERT models are usually too heavy to be served online, in terms of both memory cost and inference speed. In this work, we focus on efficient model for polyphone disambiguation and propose a two-stage knowledge distillation method that transfers the knowledge from a heavy BERT model in both pre-training and fine-tuning stages to a lightweight BERT model, in order to reduce online serving cost. Experiments on Chinese and English polyphone disambiguation datasets demonstrate that our method reduces model parameters by a factor of 5 and improves inference speed by 7 times, while nearly matches the classification accuracy (95.4% on Chinese and 98.1% on English) to the original BERT model.