A Comparative Study on the Effort of Human Translation and Post-Editing in Relation to Text Types: An Eye-Tracking and Key-Logging Experiment

Ying Cui, Xiao Liu and Yuqin Cheng

SAGE Open, 2023, vol. 13, issue 1, 21582440231155849

Abstract: With the increasing utilization of Machine Translation, it is worth exploring in what areas it actually reduces translators’ effort. This study focuses on the English-Chinese language pair and compares the effort of human translation (HT) and that of post-editing (PE) in relation to text types, covering advertising, news, legal, and literary texts, via an eye-tracking and key-logging experiment and a follow-up questionnaire survey. It refers to Krings’ framework and explores effort in terms of temporal, cognitive, and technical dimensions. Data were obtained from 33 Chinese student translators, and data analyses lead to the following conclusions. First, PE involves less effort than HT, and PE increases productivity and improves translation quality. Second, the tendency that PE involves less effort is seen in the advertising, news, and literary text types, but the measures of effort show variation for the legal text. Third, the objective measures, including Seconds per Target Word, Edits per Target Word, Pause Count per Target Word, Pause Duration per Target Word, Source Fixation Duration per Source Word, and Source Fixation Count per Source Word, are reliable and well correlated with subjective measures.

Keywords: effort; fixation; pause; post-editing; translation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:13:y:2023:i:1:p:21582440231155849

DOI: 10.1177/21582440231155849

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