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

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, l...

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Bibliographic Details
Published inOpen Linguistics Vol. 8; no. 1; pp. 310 - 327
Main Authors Almanna, Ali, Jamoussi, Rafik
Format Journal Article
LanguageEnglish
Published De Gruyter 30.07.2022
Subjects
Arabic-English translation
aspect
cognitive linguistics
contextual tense
morphological tense
neural machine translation
post-editing
structural tense
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Summary: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.
ISSN:2300-9969
2300-9969
DOI:10.1515/opli-2022-0192