Shivashankar Subramanian, Ioana Baldini, et al.
IAAI 2020
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.
Shivashankar Subramanian, Ioana Baldini, et al.
IAAI 2020
George Kour, Samuel Ackerman, et al.
EMNLP 2022
Gabriele Picco, Lam Thanh Hoang, et al.
EMNLP 2021
Kevin Gu, Eva Tuecke, et al.
ICML 2024