Until now, interpretation has remained a profession relatively unchanged by software advances, largely because it is a nuanced art form that is, as one professional interpreter says, “as much interpersonal as it is linguistic”.
While much has been said on the topics of translation productivity tools, machine translation (MT) and measuring MT quality, comparatively little research has been done before now into how similar quality estimation (QE) methods might be applied to the interpreting arena.
A group of researchers in the US, led by Jordan Boyd-Graber of the University of Maryland and Graham Neubig of Carnegie Mellon University, has just published a paper titled Automatic Estimation of Simultaneous Interpreter Performance. Here, Slator outlines the findings of the paper and examines its implications. They also include a Q&A with the authors and comment from a professional interpreter.