@inproceedings{oncevay-etal-2025-translating,
title = "Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education",
author = "Oncevay, Arturo and
Kochkina, Elena and
Ramani, Keshav and
Aguda, Toyin and
Kaur, Simerjot and
Smiley, Charese",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1774/",
pages = "35018--35032",
ISBN = "979-8-89176-332-6",
abstract = "Domain-specific multilingual terminology is essential for accurate machine translation (MT) and cross-lingual NLP applications. We present a gold-standard terminology resource for the tax and financial education domains, built from curated governmental publications and covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. Using this resource, we assess various MT systems and LLMs on translation quality and term accuracy. We annotate over 3,000 terms for domain-specificity, facilitating a comparison between domain-specific and general term translations, and observe models' challenges with specialized tax terms. We also analyze the case of terminology-aided translation, and the LLMs' performance in extracting the translated term given the context. Our results highlight model limitations and the value of high-quality terminologies for advancing MT research in specialized contexts."
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<abstract>Domain-specific multilingual terminology is essential for accurate machine translation (MT) and cross-lingual NLP applications. We present a gold-standard terminology resource for the tax and financial education domains, built from curated governmental publications and covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. Using this resource, we assess various MT systems and LLMs on translation quality and term accuracy. We annotate over 3,000 terms for domain-specificity, facilitating a comparison between domain-specific and general term translations, and observe models’ challenges with specialized tax terms. We also analyze the case of terminology-aided translation, and the LLMs’ performance in extracting the translated term given the context. Our results highlight model limitations and the value of high-quality terminologies for advancing MT research in specialized contexts.</abstract>
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%0 Conference Proceedings
%T Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education
%A Oncevay, Arturo
%A Kochkina, Elena
%A Ramani, Keshav
%A Aguda, Toyin
%A Kaur, Simerjot
%A Smiley, Charese
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F oncevay-etal-2025-translating
%X Domain-specific multilingual terminology is essential for accurate machine translation (MT) and cross-lingual NLP applications. We present a gold-standard terminology resource for the tax and financial education domains, built from curated governmental publications and covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. Using this resource, we assess various MT systems and LLMs on translation quality and term accuracy. We annotate over 3,000 terms for domain-specificity, facilitating a comparison between domain-specific and general term translations, and observe models’ challenges with specialized tax terms. We also analyze the case of terminology-aided translation, and the LLMs’ performance in extracting the translated term given the context. Our results highlight model limitations and the value of high-quality terminologies for advancing MT research in specialized contexts.
%U https://aclanthology.org/2025.emnlp-main.1774/
%P 35018-35032
Markdown (Informal)
[Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education](https://aclanthology.org/2025.emnlp-main.1774/) (Oncevay et al., EMNLP 2025)
ACL