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.

本研究提出了一种用于处理商业任务导向型对话系统中未识别用户语句的端到端管道，包括特定定制聚类算法、新颖的聚类代表提取方法和聚类命名。通过评估这些组件，证明了它们在分析未识别用户请求方面的益处。