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Open AccessFeature PaperArticle

Post-Editing Neural MT in Medical LSP: Lexico-Grammatical Patterns and Distortion in the Communication of Specialized Knowledge

Université de Paris, CLILLAC-ARP, F-75013 Paris, France
Informatics 2019, 6(3), 26; https://doi.org/10.3390/informatics6030026
Received: 1 May 2019 / Revised: 23 June 2019 / Accepted: 27 June 2019 / Published: 30 June 2019
(This article belongs to the Special Issue Advances in Computer-Aided Translation Technology)
The recent arrival on the market of high-performing neural MT engines will likely lead to a profound transformation of the translation profession. The purpose of this study is to explore how this paradigm change impacts the post-editing process, with a focus on lexico-grammatical patterns that are used in the communication of specialized knowledge. A corpus of 109 medical abstracts pre-translated from English into French by the neural MT engine DeepL and post-edited by master’s students in translation was used to study potential distortions in the translation of lexico-grammatical patterns. The results suggest that neural MT leads to specific sources of distortion in the translation of these patterns, not unlike what has previously been observed in human translation. These observations highlight the need to pay particular attention to lexico-grammatical patterns when post-editing neural MT in order to achieve functional equivalence in the translation of specialized texts. View Full-Text
Keywords: neural MT; post-editing; functional equivalence; distortion in translation; lexico-grammatical patterns; medical LSP neural MT; post-editing; functional equivalence; distortion in translation; lexico-grammatical patterns; medical LSP
MDPI and ACS Style

Martikainen, H. Post-Editing Neural MT in Medical LSP: Lexico-Grammatical Patterns and Distortion in the Communication of Specialized Knowledge. Informatics 2019, 6, 26.

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