Translation Word-Level Auto-Completion: What can we achieve out of the box?

Moslem, Yasmin, Haque, Rejwanul and Way, Andy (2022) Translation Word-Level Auto-Completion: What can we achieve out of the box? In: Conference on Machine Translation - Proceedings. Association for Computational Linguistics, Abu Dhabi, pp. 1176-1181. ISBN 978-195942929-6

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Abstract

Research on Machine Translation (MT) has achieved important breakthroughs in several areas. While there is much more to be done in order to build on this success, we believe that the language industry needs better ways to take full advantage of current achievements. Due to a combination of factors, including time, resources, and skills, businesses tend to apply pragmatism into their AI workflows. Hence, they concentrate more on outcomes, e.g. delivery, shipping, releases, and features, and adopt high-level working production solutions, where possible. Among the features thought to be helpful for translators are sentence-level and word-level translation autosuggestion and auto-completion. Suggesting alternatives can inspire translators and limit their need to refer to external resources, which hopefully boosts their productivity. This work describes our submissions to WMT's shared task on word-level auto-completion, for the Chinese-to-English, English-to-Chinese, German-to-English, and English-to-German language directions. We investigate the possibility of using pre-trained models and out-of-the-box features from available libraries. We employ random sampling to generate diverse alternatives, which reveals good results. Furthermore, we introduce our open-source API, based on CTranslate2, to serve translations, auto-suggestions, and auto-completions. © 2022 Association for Computational Linguistics.

Item Type:	Book Section
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	12 Jun 2025 15:33
Last Modified:	12 Jun 2025 15:33
URI:	https://norma.ncirl.ie/id/eprint/7836

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