Terminology-Constrained Translation from Monolingual Data Using GRPO (2025)

Abstract: Terminology consistency is essential for high-quality machine translation, especially in domain-specific and professional contexts, where accurate term translation directly impacts usability. This paper presents the submission from the BSC team to the WMT25 Terminology-Aware Translation Task. We propose the use of GRPO (Group Relative Policy Optimization) to adapt translation models using monolingual data only, without requiring parallel corpora. Our reward function jointly optimizes for terminology adherence and overall translation quality, leveraging quality-estimation metrics. Experimental results demonstrate that our method consistently improves terminology translation across three language directions—English to Spanish, German, and Russian—by up to +0.36 Tₚ points across all evaluated models.

## introduction in recent years, large language models (llms) have emerged as the state of the art across a wide range of natural language processing tasks, including machine translation (mt). owing to their vast number of parameters, these models possess the capacity to handle multiple languages and adapt across diverse domains. however, their large scale also makes them computationally expensive to finetune or adapt to specific settings. in this work, we address the task of terminologyaware machine translation, aiming to produce domain-specific translations without the need for costly fine-tuning. rather than adapting the model through retraining, we condition it at inference time by incorporating a bilingual glossary into the translation prompt. this approach enables flexible, onthe-fly domain adaptation, assuming that relevant terminology is available. another major challenge in adapting llms to mt tasks lies in the scarcity of high-quality parallel corpora, particularly for low-resource language prompt: translate the following text from english into spanish. please ensure the following terminology is used: figure 1 : example of prompt for terminology-aware translations with a glossary. in green, source terms in english. in yellow, target translations in spanish. pairs. in many cases, obtaining sufficient parallel data for supervised training is infeasible. this paper presents the barcelona supercomputing center (bsc) submission to the terminology-aware mt task for the first track: sentence/paragraph-level translation. our system is built around two key contributions: prior work in terminology-aware machine translation has taken several different approaches. a common strategy involves fine-tuning models with terminology constraints. for instance, kim et al. (2024) extract terminology from training data to build a glossary and fine-tune the model using inputs augmented with extracted terms. zheng et al. (2024) propose dragft, a framework combining dictionary-enhanced prompting, retrievalaugmented few-shot selection, and fine-tuning to improve translation in specialized domains. another line of work focuses on synthetic data generation and post-editing. moslem et al. (2023) use llms to generate bilingual data containing prespecified terminology, which is then used to finetune mt models. they further apply llm-based post-editing to insert missing terms into system outputs that failed to adhere to terminology constraints. other methods aim to enforce terminology during decoding. bogoychev and chen (2023) explore constrained decoding strategies and llm-based paraphrasing to increase term fidelity, including the use of negative constraints that penalize incorrect term usage. reinforcement learning (rl) has also been explored as a way to improve terminology translation. li et al. (2025) integrate rl with word alignment to define reward signals, enabling models to translate key terminology without explicit term detection at inference time. our work is most closely aligned with this last line of research. the key distinction is that our approach relies solely on monolingual data and inference-time prompting, and is therefore better suited to low-resource settings where parallel corpora may be scarce or unavailable. ## methodology in this section, we will discuss our proposed method to adapt the llms to the task of terminology-aware translation, using monolingual data only. ## conclusions this paper presents the bsc team's submission to the shared task of terminology-aware machine translation. our results show that grpo training using only monolingual data can effectively adapt an llm for this task, producing translations that include the correct terminology in almost all cases. analysis of the training rewards shows the importance of including a quality-estimation term to regularize training and ensure strong translation performance. studying the impact of the choice of languages and its relationship with performance remains an avenue for future work.