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

Abstract Machine translation (MT) has made significant strides and 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 valid stage in their workflow, little been done investigate for purpose informing training PE. Against this background, present project focuses on handling tense aspect configurations English Arabic sentences using current neural machine (NMT) systems. Using dataset representative sentences, five NMT engines was assessed against reference translations. The investigation reveals regressing when comparing morphological, structural, contextual tenses. These findings are believed represent valuable information contributes more informed Arabic-into-English output.