Laniqo at WMT 25 Terminology Translation Task: A Multi-Objective Reranking Strategy for Terminology-Aware Translation via P areto-Optimal Decoding (2025)

Abstract: This paper describes the Laniqo system submitted to the WMT25 Terminology Translation Task. Our approach uses a Large Language Model fine-tuned on parallel data augmented with source-side terminology constraints. To select the final translation from a set of generated candidates, we introduce Pareto-Optimal Decoding - a multi-objective reranking strategy. This method balances translation quality with term accuracy by leveraging several quality estimation metrics alongside Term Success Rate (TSR). Our system achieves TSR greater than 0.99 across all language pairs on the Shared Task testset, demonstrating the effectiveness of the proposed approach.

## introduction the shared task consists of two tracks: sentencelevel translation and document-level translation. our submission focuses exclusively on the sentence-level track, where each sentence is translated independently using provided terminology constraints. the shared task requires systems to be evaluated in three distinct modes: our system builds upon the eurollm-9b-instruct model 1 (martins et al., 1 https://huggingface.co/utter-project/ eurollm-9b-instruct 2025), which served as the baseline for our experiments in terminology-aware machine translation (mt). while this model already offers multilingual translation capabilities, it is not explicitly designed to handle translation with domain-specific terminology constraints. to further improve terminology control and translation quality, we explored several complementary strategies: before translation, we replaced sourcelanguage terms with their corresponding target-language equivalents. this was combined with explicit prompts designed to guide the model in retaining or correctly adapting the inserted terms. ## related work handling specialized terminology in neural machine translation (nmt) has received considerable research interest in recent years. the proposed methods can be broadly classified into three main categories: the emergence of multilingual large language models (llms), such as eurollm @xcite and tower+ @xcite , represents a significant shift in mt towards the llm era @xcite . the ability of llms to follow natural language instructions embedded within a prompt opens up new possibilities for adhering to terminology constraints. several possible strategies that leverage this capability have been explored. one approach involves using llms to generate term-rich synthetic data for fine-tuning traditional nmt models @xcite . another line of work uses llms for automatic post-editing, prompting the model to inject missing terms into an existing translation (bogoychev and chen, 2023). a third, more direct method involves providing the llm with the source text and a list of terminology constraints which must be included in the output @xcite . our approach builds on this direct prompting method by extending it through the integration of constraints in the source text. while the primary objective of the shared task is to ensure the correct translation of specified terminology, maintaining the overall quality of the translation remains a critical factor. well-established methods for improving the quality of machine translation, such as minimum bayes risk (mbr) decoding @xcite and qe reranking, have consistently demonstrated their effectiveness in various research studies in recent years @xcite @xcite . however, these approaches are typically designed to optimize for a single metric. this task requires the simultaneous optimization of two distinct aspects: term accuracy and general translation quality. to address this multi-objective problem, we propose a method that balances these potentially conflicting criteria. ## results initially, we conducted experiments on the wmt25 development dataset. the results obtained using the replace method were used as our baseline. as in addition, we performed statistical tests using the paired bootstrap resampling method @xcite . we sampled @xmath0.4 * testset_length segments and p-value @xmath1.05. each subsequent processing stage was compared to the previous one. statistically significant differences are marked with an asterisk (*) in @xmath4, as well as on the entire dataset according to the comet, bleu, and chrf metrics, was the method using lora with a new prompt. the other methods yield improvements when the averaged metric results are compared, but these are not statistically significant. experiments showed that the best results were achieved by using a fine-tuned model together with a modified prompt and few-shot examples, as well as by employing the pareto-optimal decoding method along with the xcomet and remedy metrics. these methods were used to translate the wmt25 test dataset, except for few-shot prompting, which was found not to affect the translation quality when combined with pareto-optimal decoding. the final results are presented in the final results of all the calculated metrics demonstrate that the proposed method performs well across all evaluated metrics. in particular, tsr achieves values above 0.99 in both the proper and random dataset modes across all considered directions. in @xmath5.0 in the proper dataset mode, which means that the entire specified terminology in the dataset was transferred correctly.