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Part of
Recent Advances in Multiword Units in Machine Translation and Translation Technology
Edited by Johanna Monti, Gloria Corpas Pastor, Ruslan Mitkov and Carlos Manuel Hidalgo-Ternero
[Current Issues in Linguistic Theory 366] 2024
► pp. 57–78

Chapter 4
Post-editing neural machine translation in specialised languages

The role of corpora in the translation of phraseological structures

 | 1Université Paris Cité, CLILLAC-ARP
 | 2ESIT / Université Sorbonne Nouvelle, CLESTHIA
 | 1Université Paris Cité, CLILLAC-ARP
 | 1Université Paris Cité, CLILLAC-ARP
This study focuses on phraseology in specialised texts and on students’ difficulties pertaining to phraseology in post-editing neural machine translation output. It is undertaken within the corpus-based methodological framework that we have developed for several purposes, one of which being to assess the impact of corpus use on translation and post-editing. The objective of the study is to propose a descriptive analysis of typical student errors related to phraseology in order to design tailored pedagogical materials. We aim to show that, with consistent training in querying corpora and in interpreting results in an appropriate manner, students can manage to improve their productions when translating specialised texts or when post-editing machine translation output.
Keywords: phraseology, neural machine translation, post-editing, errors, corpus-based methodology, specialized texts
Article outline
  • 1.Introduction
  • 2.Theoretical background
    • 2.1Corpora in translation training
    • 2.2Machine translation and post-editing
    • 2.3Phraseology in LSP and NMT
  • 3.Context, methods and data
    • 3.1Context
    • 3.2Methods and data
  • 4.Analysis of typical student errors in post-editing phraseology
    • 4.1Type 1: Overconfidence in MT (or under-editing of MT output)
    • 4.2Type 2: Underconfidence in MT (or over-editing of MT output)
    • 4.3Type 3: Failure to correct MT output
  • 5.Constructing classroom activities
  • 6.Towards the analysis of NMT output on MWUs
  • 7.Conclusion
  • Notes
  • References
Published online: 7 November 2024
DOI logo
https://doi.org/10.1075/cilt.366.04kub
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