Introducing Quality Estimation to Machine Translation Post-editing Workflow: An Empirical Study on Its Usefulness
This is an incremental study that addresses improving productivity for translators in machine translation workflows by integrating QE.
This study examined the usefulness of sentence-level Quality Estimation (QE) in English-Chinese Machine Translation Post-Editing (MTPE), finding that QE significantly reduces post-editing time, with no significant interaction effects across MT quality or translator expertise levels.
This preliminary study investigates the usefulness of sentence-level Quality Estimation (QE) in English-Chinese Machine Translation Post-Editing (MTPE), focusing on its impact on post-editing speed and student translators' perceptions. It also explores the interaction effects between QE and MT quality, as well as between QE and translation expertise. The findings reveal that QE significantly reduces post-editing time. The examined interaction effects were not significant, suggesting that QE consistently improves MTPE efficiency across medium- and high-quality MT outputs and among student translators with varying levels of expertise. In addition to indicating potentially problematic segments, QE serves multiple functions in MTPE, such as validating translators' evaluations of MT quality and enabling them to double-check translation outputs. However, interview data suggest that inaccurate QE may hinder post-editing processes. This research provides new insights into the strengths and limitations of QE, facilitating its more effective integration into MTPE workflows to enhance translators' productivity.