Towards Predicting Post-editing Effort with Source Text Readability

Abstract

This paper investigates the impact of source text readability on the effort of post-editing English-Chinese Neural Machine Translation (NMT) output. Six readability formulas, including both traditional and newer ones, were employed to measure readability, and their predictive power towards post-editing effort was evaluated. Keystroke logging, self-report questionnaires, and retrospective protocols were applied to collect the data of post-editing for general text type from thirty-four student translators. The results reveal that: 1) readability has a significant yet weak effect on cognitive effort, while its impact on temporal and technical effort is less pronounced; 2) high NMT quality may alleviate the effect of readability; 3) readability formulas have the ability to predict post-editing effort to a certain extent, and newer formulas such as the Crowdsourced Algorithm of Reading Comprehension (CAREC) outperformed traditional formulas in most cases. Apart from readability formulas, the study shows that some fine-grained reading-related linguistic features are good predictors of post-editing time. Finally, this paper provides implications for automatic effort estimation in the translation industry.

Keywords

• Neural machine translation
• post-editing effort
• readability
• key logging
• student translator