DaCoM: Strategies to Construct Domain-specific Low-resource Language Machine Translation Dataset
Junghoon Kang, Keunjoo Tak, Joungsu Choi, Myunghyun Kim, Junyoung Jang, Youjin Kang
Abstract
Translation of low-resource languages in industrial domains is essential for improving market productivity and ensuring foreign workers have better access to information. However, existing translators struggle with domain-specific terms, and there is a lack of expert annotators for dataset creation. In this work, we propose DaCoM, a methodology for collecting low-resource language pairs from industrial domains to address these challenges. DaCoM is a hybrid translation framework enabling effective data collection. The framework consists of a large language model and neural machine translation. Evaluation verifies existing models perform inadequately on DaCoM-created datasets, with up to 53.7 BLEURT points difference depending on domain inclusion. DaCoM is expected to address the lack of datasets for domain-specific low-resource languages by being easily pluggable into future state-of-the-art models and maintaining an industrial domain-agnostic approach.
BibTeX
@inproceedings{kang-etal-2025-dacom,
title = "{D}a{C}o{M}: Strategies to Construct Domain-specific Low-resource Language Machine Translation Dataset",
author = "Kang, Junghoon and
Tak, Keunjoo and
Choi, Joungsu and
Kim, Myunghyun and
Jang, Junyoung and
Kang, Youjin",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.53/",
pages = "612--624"
}