Papers by Tomohiro Yamasaki
Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models (2022.naacl-main)
Copied to clipboard
| Challenge: | Grapheme-to-phoneme conversion is a task of converting grapheme sequences into phoneme sequence. |
| Approach: | They propose a Thai grapheme-to-phoneme conversion method that uses neural networks to predict the similarity between a candidate and the correct pronunciation. |
| Outcome: | The proposed method can be applied to other languages than Thai . it is comparable to encoder-decoder models in accuracy and accuracy, it shows . |
VE-KD: Vocabulary-Expansion Knowledge-Distillation for Training Smaller Domain-Specific Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | VE-KD is a method that balances knowledge distillation and vocabulary expansion with the aim of training efficient domain-specific language models. |
| Approach: | They propose a method that balances knowledge distillation and vocabulary expansion with the aim of training efficient domain-specific language models. |
| Outcome: | VE-KD outperforms DistilBERT and Adapt-and-Distill in biomedical domain tasks . compared with other methods, it outperformed Distilbert and adapted-and distill . |