Machine translation and legal terminology
Data-driven approaches to contextual accuracy
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.
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
References (91)
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