  The majority of Chinese characters are monophonic, i.e.their pronunciations
are unique and thus can be induced easily using a check table. As for their
counterparts, polyphonic characters have more than one pronunciation. To
perform linguistic computation tasks related to spoken Mandarin Chinese, the
correct pronunciation for each polyphone must be identified among several
candidates according to its context. This process is called Polyphone
Disambiguation, a key procedure in the Grapheme-to-phoneme (G2P) conversion
step of a Chinese text-to-speech (TTS) system. The problem is well explored
with both knowledge-based and learning-based approaches, yet it remains
challenging due to the lack of publicly available datasets and complex language
phenomenon concerned polyphone. In this paper, we propose a novel
semi-supervised learning (SSL) framework for Mandarin Chinese polyphone
disambiguation that can potentially leverage unlimited unlabeled text data. We
explore the effect of various proxy labeling strategies including
entropy-thresholding and lexicon-based labeling. As for the architecture, a
pre-trained model of Electra is combined with Convolution BLSTM layers to
fine-tune on our task. Qualitative and quantitative experiments demonstrate
that our method achieves state-of-the-art performance in Mandarin Chinese
polyphone disambiguation. In addition, we publish a novel dataset specifically
for the polyphone disambiguation task to promote further researches.
