  Most Chinese Grapheme-to-Phoneme (G2P) systems employ a three-stage framework
that first transforms input sequences into character embeddings, obtains
linguistic information using language models, and then predicts the phonemes
based on global context about the entire input sequence. However, linguistic
knowledge alone is often inadequate. Language models frequently encode overly
general structures of a sentence and fail to cover specific cases needed to use
phonetic knowledge. Also, a handcrafted post-processing system is needed to
address the problems relevant to the tone of the characters. However, the
system exhibits inconsistency in the segmentation of word boundaries which
consequently degrades the performance of the G2P system. To address these
issues, we propose the Reinforcer that provides strong inductive bias for
language models by emphasizing the phonological information between neighboring
characters to help disambiguate pronunciations. Experimental results show that
the Reinforcer boosts the cutting-edge architectures by a large margin. We also
combine the Reinforcer with a large-scale pre-trained model and demonstrate the
validity of using neighboring context in knowledge transfer scenarios.
