Large language models (LLMs) have demonstrated strong performance in translation tasks. Subtitle translation presents unique challenges, such as preserving the original work’s worldview and the distinctive speaking styles of its characters. Achieving high-quality translations that reflect these stylistic nuances typically requires bilingual data for a specific movie, which is often scarce or unavailable. Thus, we propose a data augmentation method that uses LLMs to improve translation performance for specific movies, even when only a few hundred bilingual sentence pairs are available. The method expands source-side data by rewriting original subtitles using information that can be extracted from the context, such as character profiles and scene descriptions, to maintain the tone and thematic consistency of the movie. For translation, the augmented sentences are aligned with manually translated originals using structural similarity, which enables style-preserving bilingual data generation via one-shot learning. Experimental results show that data augmented using the proposed method effectively improves BLEU scores for film subtitle translation, and achieves superior stylistic quality in human evaluation.

This paper describes our submission to the Japanese → English Article-level News Translation Task at WAT 2025. In this task, participants were provided with a small but high-quality parallel corpus along with two intermediate English translations: a literal translation and a style-adapted translation. To effectively exploit these limited training data, our system employs a large language model (LLM) trained via supervised fine-tuning (SFT) followed by Direct Preference Optimization (DPO) that is a preference learning technique for aligning model outputs with professional-quality references. By leveraging literal and style-adapted intermediate translations as negative (rejected) samples and human-edited English articles as positive (chosen) samples in DPO training, we achieved notable improvements in translation quality. We evaluate our approach using BLEU scores and human assessments.