Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education
Arturo Oncevay, Elena Kochkina, Keshav Ramani, Toyin Aguda, Simerjot Kaur, Charese Smiley
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.
BibTeX
@inproceedings{emnlp2025_translatingdomai,
title = {Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education},
author = {Arturo Oncevay and Elena Kochkina and Keshav Ramani and Toyin Aguda and Simerjot Kaur and Charese Smiley},
booktitle = {EMNLP 2025},
year = {2025}
}