  The majority of Chinese characters are monophonic, while a special group of
characters, called polyphonic characters, have multiple pronunciations. As a
prerequisite of performing speech-related generative tasks, the correct
pronunciation must be identified among several candidates. This process is
called Polyphone Disambiguation. Although the problem has been well explored
with both knowledge-based and learning-based approaches, it remains challenging
due to the lack of publicly available labeled datasets and the irregular nature
of polyphone in Mandarin Chinese. 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. Qualitative and quantitative
experiments demonstrate that our method achieves state-of-the-art performance.
In addition, we publish a novel dataset specifically for the polyphone
disambiguation task to promote further researches.
