  Simultaneous interpretation, translation of the spoken word in real-time, is
both highly challenging and physically demanding. Methods to predict
interpreter confidence and the adequacy of the interpreted message have a
number of potential applications, such as in computer-assisted interpretation
interfaces or pedagogical tools. We propose the task of predicting simultaneous
interpreter performance by building on existing methodology for quality
estimation (QE) of machine translation output. In experiments over five
settings in three language pairs, we extend a QE pipeline to estimate
interpreter performance (as approximated by the METEOR evaluation metric) and
propose novel features reflecting interpretation strategy and evaluation
measures that further improve prediction accuracy.
