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    <abstract>In Japanese, the form of utterances often reflect speaker-specific character traits, such as gender and personality, through the choise of linguistic elements including personal pronouns and sentence-final particles. However, such elements are not always available in English and a character’s traits are often not directly expressed in English utterances, which can lead to character-inconsistent translations of English novels into Japanese. To address this, we propose a character-aware translation framework that incorporates speaker embeddings. We first train a speaker embedding model by masking the expressions in Japanese utterances that manifest the speaker’s traits and learning to predict them. The resulting embeddings are then injected into a machine translation model. Experimental results show that our proposed method outperforms conventional fine-tuning in preserving speaker-specific character traits in translations.</abstract>
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%0 Conference Proceedings
%T Character-Aware English-to-Japanese Translation of Fictional Dialogue Using Speaker Embeddings and Back-Translation
%A Nagato, Ayuna
%A Matsuzaki, Takuya
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Tenth Conference on Machine Translation
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-341-8
%F nagato-matsuzaki-2025-character
%X In Japanese, the form of utterances often reflect speaker-specific character traits, such as gender and personality, through the choise of linguistic elements including personal pronouns and sentence-final particles. However, such elements are not always available in English and a character’s traits are often not directly expressed in English utterances, which can lead to character-inconsistent translations of English novels into Japanese. To address this, we propose a character-aware translation framework that incorporates speaker embeddings. We first train a speaker embedding model by masking the expressions in Japanese utterances that manifest the speaker’s traits and learning to predict them. The resulting embeddings are then injected into a machine translation model. Experimental results show that our proposed method outperforms conventional fine-tuning in preserving speaker-specific character traits in translations.
%U https://aclanthology.org/2025.wmt-1.10/
%P 180-190

Markdown (Informal)

[Character-Aware English-to-Japanese Translation of Fictional Dialogue Using Speaker Embeddings and Back-Translation](https://aclanthology.org/2025.wmt-1.10/) (Nagato & Matsuzaki, WMT 2025)

• Character-Aware English-to-Japanese Translation of Fictional Dialogue Using Speaker Embeddings and Back-Translation (Nagato & Matsuzaki, WMT 2025)

ACL

• Ayuna Nagato and Takuya Matsuzaki. 2025. Character-Aware English-to-Japanese Translation of Fictional Dialogue Using Speaker Embeddings and Back-Translation. In Proceedings of the Tenth Conference on Machine Translation, pages 180–190, Suzhou, China. Association for Computational Linguistics.