@inproceedings{guttmann-etal-2025-laniqo-wmt25,
    title = "Laniqo at {WMT}25 Terminology Translation Task: A Multi-Objective Reranking Strategy for Terminology-Aware Translation via {P}areto-Optimal Decoding",
    author = "Guttmann, Kamil  and
      Charkiewicz, Adrian  and
      Rostek, Zofia  and
      Pokrywka, Miko{\l}aj  and
      Nowakowski, Artur",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.wmt-1.107/",
    pages = "1276--1283",
    ISBN = "979-8-89176-341-8",
    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."
}