We present the preliminary findings of the WAT 2025 shared task on document-level translation from Japanese to English in the news domain. This task focuses on translating full articles with particular attention to whether translation models can learn to produce expressions and stylistic features typical of English news writing, with the aim to generate outputs that resemble original English news articles. The task consists of three translation styles: (1) literal translation, (2) news-style translation, based on English articles edited to match Japanese content, and (3) finalized translation, the primary goal of this shared task. Only one team participated and submitted a system to a single subtask. All tasks were evaluated automatically, and one task was also evaluated manually to compare the submission with the baseline.

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.