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- W4223456894 abstract "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." @default.
- W4223456894 created "2022-04-14" @default.
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- W4223456894 date "2022-04-11" @default.
- W4223456894 modified "2023-09-26" @default.
- W4223456894 title "Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems" @default.
- W4223456894 doi "https://doi.org/10.48550/arxiv.2204.05158" @default.
- W4223456894 hasPublicationYear "2022" @default.
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