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Text-to-Text Transfer Transformer (T5) has recently been considered for theGrapheme-to-Phoneme (G2P) transduction. As a follow-up, a tokenizer-freebyte-level model based on T5 referred to as ByT5, recently gave promisingresults on word-level G2P conversion by representing each input character withits corresponding UTF-8 encoding. Although it is generally understood thatsentence-level or paragraph-level G2P can improve usability in real-worldapplications as it is better suited to perform on heteronyms and linking soundsbetween words, we find that using ByT5 for these scenarios is nontrivial. SinceByT5 operates on the character level, it requires longer decoding steps, whichdeteriorates the performance due to the exposure bias commonly observed inauto-regressive generation models. This paper shows that the performance ofsentence-level and paragraph-level G2P can be improved by mitigating suchexposure bias using our proposed loss-based sampling method.

Mitigating the Exposure Bias in Sentence-Level Grapheme-to-Phoneme (G2P) Transduction