Masaki Hamada — accepted AI-conference papers

2025

Exploring Context Strategies in LLMs for Discourse-Aware Machine Translation

Ritvik Choudhary, Rem Hida, Masaki Hamada, Hayato Futami, Toshiyuki Sekiya

EMNLP 2025

While large language models (LLMs) excel at machine translation (MT), the impact of how LLMs utilize different forms of contextual information on discourse-level phenomena remains underexplored. We systematically investigate how different forms of context such as prior source sentences, models’ gene

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2022

Polyphone Disambiguation and Accent Prediction Using Pre-Trained Language Models in Japanese TTS Front-End

Rem Hida, Masaki Hamada, Chie Kamada, Emiru Tsunoo, Toshiyuki Sekiya, Toshiyuki Kumakura

ICASSP 2022accepted

Although end-to-end text-to-speech (TTS) models can generate natural speech, challenges still remain when it comes to estimating sentence-level phonetic and prosodic information from raw text in Japanese TTS systems. In this paper, we propose a method for polyphone disambiguation (PD) and accent pre…

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