@inproceedings{fbf5f9cf54e04b80ae8c6dc2e562310b,
title = "Instance-Based Domain Adaptation for Improving Terminology Translation",
abstract = "Terms are essential indicators of a domain, and domain term translation is dealt with priority in any translation workflow. Translation service providers who use machine translation (MT) expect term translation to be unambiguous and consistent with the context and domain in question. Although current state-of-the-art neural MT (NMT) models are able to produce high-quality translations for many languages, they are still not at the level required when it comes to translating domain-specific terms. This study presents a terminology-aware instance-based adaptation method for improving terminology translation in NMT. We conducted our experiments for French-to-English and found that our proposed approach achieves a statistically significant improvement over the baseline NMT system in translating domain-specific terms. Specifically, the translation of multi-word terms is improved by 6.7% over a strong baseline.",
author = "Prashanth Nayak and Rejwanul Haque and Kelleher, {John D.} and Andy Way",
note = "Publisher Copyright: {\textcopyright} 2023 The authors. This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0); 19th Machine Translation Summit, MT Summit 2023 ; Conference date: 04-09-2023 Through 08-09-2023",
year = "2023",
month = sep,
day = "8",
language = "English",
series = "MT Summit 2023 - Proceedings of 19th Machine Translation Summit",
publisher = "Asia-Pacific Association for Machine Translation",
pages = "222--234",
editor = "Masao Utiyama and Rui Wang",
booktitle = "Research Track",
}