  Simultaneous interpretation, the translation of speech from one language to
another in real-time, is an inherently difficult and strenuous task. One of the
greatest challenges faced by interpreters is the accurate translation of
difficult terminology like proper names, numbers, or other entities.
Intelligent computer-assisted interpreting (CAI) tools that could analyze the
spoken word and detect terms likely to be untranslated by an interpreter could
reduce translation error and improve interpreter performance. In this paper, we
propose a task of predicting which terminology simultaneous interpreters will
leave untranslated, and examine methods that perform this task using supervised
sequence taggers. We describe a number of task-specific features explicitly
designed to indicate when an interpreter may struggle with translating a word.
Experimental results on a newly-annotated version of the NAIST Simultaneous
Translation Corpus (Shimizu et al., 2014) indicate the promise of our proposed
method.
