Title: Understanding the Experience of Interactive Machine Translation
An increasing amount of information is being translated using Machine Translation. Despite major improvements in quality, intervention is still required by a human if quality is to be trusted. This intervention takes the form of “post-editing”, i.e. identifying and quickly fixing MT errors. This tends to be done in a serial manner, that is, the source text is sent to the MT engine, an MT suggestion appears, and the editor assesses it and fixes it, if necessary (termed “traditional” post-editing). Recently, a new modality has been developed called Interactive Machine Translation (IMT), involving real-time adaptation of the MT suggestion as the editor makes choices and implements edits. Very little is understood about this new form of human-machine interaction. This PhD will fill this knowledge gap by researching the cognitive effort involved in this type of translation production; how perceptions of system competence and trust influences decision-making; and how these evolve over time and experience.