  Polyphone disambiguation is the most crucial task in Mandarin
grapheme-to-phoneme (g2p) conversion. Previous studies have approached this
problem using pre-trained language models, restricted output, and extra
information from Part-Of-Speech (POS) tagging. Inspired by these strategies, we
propose a novel approach, called g2pW, which adapts learnable softmax-weights
to condition the outputs of BERT with the polyphonic character of interest and
its POS tagging. Rather than using the hard mask as in previous works, our
experiments show that learning a soft-weighting function for the candidate
phonemes benefits performance. In addition, our proposed g2pW does not require
extra pre-trained POS tagging models while using POS tags as auxiliary features
since we train the POS tagging model simultaneously with the unified encoder.
Experimental results show that our g2pW outperforms existing methods on the
public CPP dataset. All codes, model weights, and a user-friendly package are
publicly available.
