SYSTRAN @ WMT 2025 General Translation Task (2025)

Abstract: We present an English-to-Japanese translationsystem built upon the EuroLLM-9B (Martinset al., 2025) model. The training process involvestwo main stages: continue pretraining(CPT) and supervised fine-tuning (SFT). Afterboth stages, we further tuned the model using adevelopment set to optimize performance. Fortraining data, we employed both basic filteringtechniques and high-quality filtering strategiesto ensure data cleanness. Additionally, we classifyboth the training data and development datainto four different domains and we train andfine-tune with domain specific prompts duringsystem training. Finally, we applied MinimumBayes Risk (MBR) decoding and paragraph-levelreranking for post-processing to enhancetranslation quality.

## introduction large language models (llms) are increasingly used in machine translation, taking advantage of their deep understanding of both source and target languages. their training typically involves two main stages: continued pretraining (cpt) and supervised fine-tuning (sft). in the pretraining stage, the model is exposed to massive amounts of unlabeled text and learns by predicting the next token in a sequence. this allows the model to acquire a broad understanding of language structure, grammar, general world knowledge, and reasoning patterns. in the supervised finetuning stage, the model is trained on task-specific labeled datasets, where each input is paired with reference output. this targeted training enables the model to follow instructions more precisely and handle specialized tasks, such as question answering, summarization, classification, and translation, with greater accuracy. for the wmt25 general translation task, we began with the pretrained llm eurollm-9b @xcite and performed additional training using bilingual corpora containing only english-japanese sentence pairs. in this stage, we employed the two aforementioned training approaches: continued pretraining (cpt) and supervised finetuning (sft), and trained separate systems using each method. following the first stage, a reduced development dataset was employed to fine-tune (ft) the systems to the wmt translation tasks. the training architecture is shown in figure 1 (left side). before generating translations, we segment the wmt25 test set into individual sentences using the newline character ("\n"), as our systems are trained to operate at the sentence level. during inference, we apply minimum bayes risk (mbr) decoding and reranking of diverse translation hypotheses produced by our two models. for each input sentence, we generate up to 300 translation candidates by combining outputs from both trained models with variations in decoding prompts ( as a result, our submission is an ensemble of two open-weight, sentence-level, english-to-japanese translation models with a combined total of 18b parameters. the following sections describe the data preparation process as well as give additional details on the training procedures used to build our wmt25 english-japanese system. ## decoding and postprocessing we follow the idea of quality-aware decoding proposed by @xcite . first, we apply quality estimation (qe) to discard the translation candidates with the lowest quality. then, we perform minimum bayes risk (mbr) decoding over the remaining hypotheses, using cometkiwi @xcite as the loss function. the candidate with the lowest expected risk is selected as the final output. to generate diverse output for each input, we apply different domain-free/domain-specific prompts, zero/few-shot examples, and greedy decoding as well as nucleus decoding. ## conclusions in this paper, we present our english-to-japanese translation system for the wmt25 general translation task. the final output is generated by ensembling two models trained with different strategies: continued pretraining (cpt) and supervised finetuning (sft). both models are trained on a cleaned parallel corpora of 10 million sentence pairs and further fine-tuned (ft) on a development set consisting of 5,736 sentences. mbr and re-ranking inference post-processing are also successfully performed to obtain the final quality boost. additionally, we release one classification model foot_5 and two translation models foot_6 in our huggingface repository.