@inproceedings{rabinovich-etal-2022-gaining,
title = "Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems",
author = "Rabinovich, Ella and
Vetzler, Matan and
Boaz, David and
Kumar, Vineet and
Pandey, Gaurav and
Anaby Tavor, Ateret",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.22",
doi = "10.18653/v1/2022.emnlp-industry.22",
pages = "218--225",
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 fails 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.",
}
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<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 fails 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.</abstract>
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%0 Conference Proceedings
%T Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems
%A Rabinovich, Ella
%A Vetzler, Matan
%A Boaz, David
%A Kumar, Vineet
%A Pandey, Gaurav
%A Anaby Tavor, Ateret
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F rabinovich-etal-2022-gaining
%X 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 fails 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.
%R 10.18653/v1/2022.emnlp-industry.22
%U https://aclanthology.org/2022.emnlp-industry.22
%U https://doi.org/10.18653/v1/2022.emnlp-industry.22
%P 218-225
Markdown (Informal)
[Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems](https://aclanthology.org/2022.emnlp-industry.22) (Rabinovich et al., EMNLP 2022)
ACL