NTTSU at WMT 2025 General Translation Task (2025)

Abstract: This paper presents the submission of NTTSU for the constrained track of the English–Japanese and Japanese–Chinese at the WMT2025 general translation task.For each translation direction, we build translation models from a large language model by combining continual pretraining, supervised fine-tuning, and preference optimization based on the translation quality and adequacy.We finally generate translations via context-aware MBR decoding to maximize translation quality and document-level consistency.

## introduction we describe our nttsu translation system in the wmt'25 english-japanese (en-ja) and japanese-chinese (ja-zh) general translation task under the constrained track. our translation models are trained on a pretraining large language model (llm), qwen3-14b @xcite . we combine training methods for each translation direction from three training stages: continual pretraining (cpt) @xcite , supervised fine-tuning (sft) @xcite , and preference optimization (po) @xcite . in po, to maximize the translation quality and adequacy, we use two different reward metrics, metricx-24 @xcite and coverage of word alignment between source and target texts @xcite . after training the models, we generate translations using context-aware minimum bayes risk (mbr) decoding, which maximizes the expected translation quality @xcite ) and also utilizes context information of both source and generated target texts, though we use a sentence-level metric @xcite . the following sections show the details of our system. ## submission system we train en-ja and ja-zh translation models from a pretrained llm, qwen3-14b (qwen team, 2025). according to our preliminary experiments and subjective judgment, we selected the combinations of training methods. finally, we generate translations via context-aware mbr decoding. we show the system overview in figure 1 and ## conclusion we built our system on the wmt'25 general translation task in en-ja and ja-zh. our models were trained with the combinations of cpt, sft, and stepwise po based on the quality-and adequacyaware rewards, for each translation direction. to maximize the translation quality and documentlevel consistency, we generated translations via context-aware mbr decoding. in document-level translation, we observed that llms are more likely to generate collapsed hallucination texts. to mitigate this issue, we employed adequacy-aware po. nevertheless, in some cases, the models still failed to generate translations. we hope to further improve hallucination mitigation in document translation.