Translating science fiction in a CAT tool: machine translation and segmentation settings

Lucas Nunes Vieira, Natalie Zelenka, Roy Youdale, Xiaochun Zhang, Michael Carl


There is increasing interest in machine assistance for literary translation, but research on how computer-assisted translation (CAT) tools and machine translation (MT) combine in the translation of literature is still incipient, especially for non-European languages. This article presents two exploratory studies where English-to-Chinese translators used neural MT to translate science fiction short stories in Trados Studio. One of the studies compares post-editing with a ‘no MT’ condition. The other examines two ways of presenting the texts on screen for post-editing, namely by segmenting them into paragraphs or into sentences. We collected the data with the Qualititivity plugin for Trados Studio and describe a method for analysing data collected with this plugin through the translation process research database of the Center for Research in Translation and Translation Technology (CRITT). While post-editing required less technical effort, we did not find MT to be appreciably timesaving. Paragraph segmentation was associated with less post-editing effort on average, though with high translator variability. We discuss the results in the light of broader concepts, such as status-quo bias, and call for more research on the different ways in which MT may assist literary translation, including its use for comparison purposes or, as mentioned by a participant, for ‘inspiration’.


Literary translation; post-editing; machine translation; neural machine translation; computer-assisted translation; CAT tools; science fiction; Chinese translation

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