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NMT verb rendering: A cognitive approach to informing Arabic-into-English post-editing

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journal contribution
submitted on 2023-03-15, 11:51 and posted on 2023-07-13, 05:38 authored by Ali Almanna, Rafik Jamoussi

Machine translation (MT) has made significant strides and has reached accuracy levels that often make the post-editing (PE) of MT output a viable alternative to manual translation. However, despite professional translators increasingly considering PE as a valid stage in their translation workflow, little has been done to investigate MT output for the purpose of informing training in PE. Against this background, the present project focuses on the handling of tense and aspect configurations in the English translation of Arabic sentences using current neural machine translation (NMT) systems. Using a dataset of representative Arabic sentences, the output of five NMT engines was assessed against reference translations. The investigation reveals regressing accuracy levels when comparing morphological, structural, and contextual tenses. These findings are believed to represent valuable information that contributes to a more informed training in the PE of Arabic-into-English NMT output. 

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Published in: Open Linguistics
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Open Access funding provided by the Qatar National Library



  • English


De Gruyter

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Humanities and Social Sciences - HBKU