@inproceedings{zhang-etal-2026-tat,
title = "{T}a{T} at {WMT}26 Terminology Translation Shared Task",
author = "Zhang, Fan and
Mei, Tu and
Mengchao, Zhang and
Wu, Jinting and
Zhang, Bowen",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.124/",
pages = "1892--1898",
ISBN = "979-8-89176-474-3",
abstract = "The accurate translation of domain-specific terminology remains a critical bottleneck for ensuring the fidelity and professionalism of machine translation systems. This paper presents our submission to the WMT26 Terminology Translation Shared Task: the TaT (Terminology-aware Translation) system. Built upon a structured, pipelined architecture, TaT integrates five specialized components designed to seamlessly ingest, process, and translate ambiguous textual inputs while strictly adhering to targeted terminology dictionary. Specifically, the framework comprises a sentence splitter, a terminology retriever, a lexical disambiguator, a baseline translator, and a dedicated terminology extractor tailored for Track 2 constraints. Both internal benchmarking and ablation studies demonstrate that our decoupled pipeline delivers robust performance, successfully meeting design expectations and substantially mitigating terminology omission and misalignment errors in domain-specific translations."
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<abstract>The accurate translation of domain-specific terminology remains a critical bottleneck for ensuring the fidelity and professionalism of machine translation systems. This paper presents our submission to the WMT26 Terminology Translation Shared Task: the TaT (Terminology-aware Translation) system. Built upon a structured, pipelined architecture, TaT integrates five specialized components designed to seamlessly ingest, process, and translate ambiguous textual inputs while strictly adhering to targeted terminology dictionary. Specifically, the framework comprises a sentence splitter, a terminology retriever, a lexical disambiguator, a baseline translator, and a dedicated terminology extractor tailored for Track 2 constraints. Both internal benchmarking and ablation studies demonstrate that our decoupled pipeline delivers robust performance, successfully meeting design expectations and substantially mitigating terminology omission and misalignment errors in domain-specific translations.</abstract>
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%0 Conference Proceedings
%T TaT at WMT26 Terminology Translation Shared Task
%A Zhang, Fan
%A Mei, Tu
%A Mengchao, Zhang
%A Wu, Jinting
%A Zhang, Bowen
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F zhang-etal-2026-tat
%X The accurate translation of domain-specific terminology remains a critical bottleneck for ensuring the fidelity and professionalism of machine translation systems. This paper presents our submission to the WMT26 Terminology Translation Shared Task: the TaT (Terminology-aware Translation) system. Built upon a structured, pipelined architecture, TaT integrates five specialized components designed to seamlessly ingest, process, and translate ambiguous textual inputs while strictly adhering to targeted terminology dictionary. Specifically, the framework comprises a sentence splitter, a terminology retriever, a lexical disambiguator, a baseline translator, and a dedicated terminology extractor tailored for Track 2 constraints. Both internal benchmarking and ablation studies demonstrate that our decoupled pipeline delivers robust performance, successfully meeting design expectations and substantially mitigating terminology omission and misalignment errors in domain-specific translations.
%U https://aclanthology.org/2026.wmt-main.124/
%P 1892-1898
Markdown (Informal)
[TaT at WMT26 Terminology Translation Shared Task](https://aclanthology.org/2026.wmt-main.124/) (Zhang et al., WMT 2026)
ACL
- Fan Zhang, Tu Mei, Zhang Mengchao, Jinting Wu, and Bowen Zhang. 2026. TaT at WMT26 Terminology Translation Shared Task. In Proceedings of the Eleventh Conference on Machine Translation, pages 1892–1898, Budapest, Hungary. Association for Computational Linguistics.