Character-Aware E nglish-to- J apanese Translation of Fictional Dialogue Using Speaker Embeddings and Back-Translation (2025)

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.

## introduction neural machine translation (nmt) has made remarkable progress in recent years. however, translating fictional narratives, such as novels, still poses substantial challenges. prior work has pointed out difficulties such as preserving long-range coherence, maintaining consistent tone across chapters, and handling figurative or culturally specific expressions @xcite @xcite . one of the less-studied issues is the preservation of character-specific linguistic style, especially in the translation of dialogue. this problem becomes particularly apparent when translating from english into japanese. japanese dialogue often encodes rich speaker characteristics -such as gender, personality, and social status -through a variety of linguistic devices, including first-and second-person pronouns, honorifics, and sentence-final particles. in contrast, english dialogue tends to be less explicit in expressing such traits. as a result, standard translation models often produce japanese utterances that contradict the original speaker's identity, leading to unnatural or inconsistent character portrayals. one major obstacle in addressing this issue is the scarcity of high-quality bilingual dialogue corpora in the literary domain. to overcome this, we employ a back-translation @xcite strategy: we first translate a large collection of japanese novels into english using a high-performing neural mt system. this enables us to construct a largescale pseudo-parallel corpus of japanese-english fictional dialogue, which serves as the foundation for training our models. while fine-tuning translation models on such indomain dialogue can partially alleviate the problem, it is insufficient to capture the nuanced stylistic variation required for faithful character portrayal. we hypothesize that explicitly modeling the speaker's identity can help resolve this issue. in this work, we propose a character-aware translation model that integrates speaker embeddings into the translation process. these embeddings are learned from japanese utterances by masking the expressions that manifest the speaker's traits and training the model to predict them, capturing latent speaker traits in a data-driven way. we inject these embeddings into a transformer-based translation model and fine-tune it on bilingual literary dialogue data. experimental results show that our approach produces translations that better preserve speakerspecific character traits compared to conventional fine-tuned baselines. the contributions of this paper are as follows: ## background: character expressiveness in japanese utterances japanese is a language rich in surface-level variation that reflects the speaker's social identity, personality, and emotional stance. in spoken language, particularly in literary dialogue, this variation is often encoded through the choice of personal pronouns, honorific expressions, and sentence-final particles. these elements do not simply convey information but actively construct the speaker's character. in this section, we outline each of these linguistic mechanisms and explain how they contribute to speaker characterization in japanese. ## experiments this section presents a comprehensive evaluation of the proposed speaker-aware translation model. we first conduct qualitative and quantitative manual analyses to assess how incorporating speaker embeddings affects character-sensitive translation aspects such as pronoun choice and sentence-final particles. next, a case study on a single character from a literary work examines consistency in character voice over multiple utterances. additionally, we perform automatic evaluation using bleu, chrf, and comet-22 scores on dialogue-heavy test data to quantify improvements over baseline models. finally, we analyze the learned speaker embeddings via principal component analysis (pca) to interpret the linguistic and stylistic features captured in the embedding space. together, these evaluations demonstrate the effectiveness of integrating speaker embeddings in enhancing the fidelity and expressiveness of literary dialogue translation. ## discussion the proposed speaker-aware translation model showed improvements in preserving speakerspecific linguistic traits. however, several limitations and potential directions for future work have emerged. ## conclusion this paper proposed a speaker-aware machine translation framework aimed at preserving characterspecific expressions in japanese literary dialogue. by constructing speaker embeddings from english descriptions of each character and incorporating them into the translation model, our method promotes more consistent and personality-aligned outputs. we evaluated our approach using the nict english-japanese translation alignment dataset. manual analysis showed that our method improves the consistency of personal pronouns and sentence-final particles, which are strongly associated with character identity. however, the use of honorifics did not improve as clearly, likely because honorific usage depends more on social context-such as the relationship between the speaker and hearer-than on character traits alone. in future work, we plan to explore the integration of situational context (e.g., dialogue participants and relationships), adopt higher-quality translation models, and refine our speaker recognition pipeline to further enhance the character consistency and fluency of literary dialogue translation.