Decisions in projects using machine translation and post-editing

Abstract

Machine translation (MT) and post-editing (PE) have become increasingly important in the professional language industry in recent years. However, not every translation job is suitable for MT and there are many options for carrying out translation/post-editing projects, e.g. no PE, light PE, full PE, full PE plus revision or translation without MT assistance. In 2019, we published a decision tree for post-editing projects (Nitzke et al. 2019) that aimed to take all considerations into account and guide the stakeholders in charge of deciding whether a job is suitable for MT and PE and, if so, what kind of quality assurance might lead to fit-for-purpose translations. To empirically test our decision tree model now, we developed a semi-structured interview with 21 questions and a scoring task addressing stakeholders who work with MT projects and have to make the decisions which are essential to our model. The interview was carried out with 19 interview partners. In the article, we discuss the interviews’ findings against the background of our model. Further, we present qualitative findings on strategic decisions, risk considerations, as well as the value of translation, working conditions and job profiles. Finally, we present our revised model motivated by the empirical findings.

Keywords

• machine translation
• post-editing
• language industry
• interview study
• best practice
• risk management