  The rapidly growing market demand for automatic dialogue agents capable of
goal-oriented behavior has caused many tech-industry leaders to invest
considerable efforts into task-oriented dialog systems. The success of these
systems is highly dependent on the accuracy of their intent identification --
the process of deducing the goal or meaning of the user's request and mapping
it to one of the known intents for further processing. Gaining insights into
unrecognized utterances -- user requests the systems fail to attribute to a
known intent -- is therefore a key process in continuous improvement of
goal-oriented dialog systems.
  We present an end-to-end pipeline for processing unrecognized user
utterances, deployed in a real-world, commercial task-oriented dialog system,
including a specifically-tailored clustering algorithm, a novel approach to
cluster representative extraction, and cluster naming. We evaluated the
proposed components, demonstrating their benefits in the analysis of
unrecognized user requests.
