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.
@article{arxiv.2204.05158,
title = {Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems},
author = {Ella Rabinovich and Matan Vetzler and David Boaz and Vineet Kumar and Gaurav Pandey and Ateret Anaby-Tavor},
journal= {arXiv preprint arXiv:2204.05158},
year = {2022}
}