@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Laniqo at WMT25 Terminology Translation Task: A Multi-Objective Reranking Strategy for Terminology-Aware Translation via Pareto-Optimal Decoding
%A Guttmann, Kamil
%A Charkiewicz, Adrian
%A Rostek, Zofia
%A Pokrywka, Mikołaj
%A Nowakowski, Artur
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Tenth Conference on Machine Translation
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-341-8
%F guttmann-etal-2025-laniqo-wmt25
%X 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.
%U https://aclanthology.org/2025.wmt-1.107/
%P 1276-1283
Markdown (Informal)
[Laniqo at WMT25 Terminology Translation Task: A Multi-Objective Reranking Strategy for Terminology-Aware Translation via Pareto-Optimal Decoding](https://aclanthology.org/2025.wmt-1.107/) (Guttmann et al., WMT 2025)
ACL