Good Neighbors Are All You Need for Chinese Grapheme-to-Phoneme Conversion

• URL: http://arxiv.org/abs/2303.07726v1
• Date: Tue, 14 Mar 2023 09:15:51 GMT
• Title: Good Neighbors Are All You Need for Chinese Grapheme-to-Phoneme Conversion
• Authors: Jungjun Kim, Changjin Han, Gyuhyeon Nam, Gyeongsu Chae
• Abstract summary: 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. We propose the Reinforcer that provides strong inductive bias for language models by emphasizing the phonological information between neighboring characters to help disambiguate pronunciations.
• Score: 1.5020330976600735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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