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Handbook of Terminology: Volume 3. Legal Terminology
Edited by Łucja Biel and Hendrik J. Kockaert
[Handbook of Terminology 3] 2023
► pp. 485–510

Machine translation and legal terminology

Data-driven approaches to contextual accuracy

| University of North Carolina at Charlotte

This chapter addresses machine translation (MT) with an eye to legal terminology. The translation of legal terms and phrasemes may be fraught with contextual complexities, and context has long been the Achilles’ heel of MT. Nevertheless, neural MT (NMT) and statistical MT (SMT) have made considerable progress in recent years, thanks to data-driven approaches making use of potentially related corpora to overcome contextual obstacles. Such approaches and the potential frozenness or repetitiveness of legal terms and phrases may allow MT to overcome some of these obstacles. This chapter reviews contextual complexities surrounding legal terminology, NMT and SMT architectures, and research on MT and legal translation to determine what might be expected from data-driven MT in this context.

Keywords: legal translation, technology, context, neural machine translation, statistical machine translation
Article outline
  • 1.Introduction
  • 2.Translation of legal terminology
    • Target language (TL) equivalents
    • ST comprehension
    • TT drafting
  • 3.Data-driven MT: SMT and NMT
  • 4.Research on machine translation of legal texts: Legal terminology
  • 5.Conclusions
  • Notes
  • References
Available under the Creative Commons Attribution-NoDerivatives (CC BY-ND) 4.0 license.

For any use beyond this license, please contact the publisher at [email protected].

Published online: 1 December 2023
DOI logo
https://doi.org/10.1075/hot.3.mac2
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