Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants
Cheng Qian, Haode Qi, Gengyu Wang, Ladislav Kunc, Saloni Potdar
Abstract
Out of Scope (OOS) detection in Conversational AI solutions enables a chatbot to handle a conversation gracefully when it is unable to make sense of the end-user query. Accurately tagging a query as out-of-domain is particularly hard in scenarios when the chatbot is not equipped to handle a topic which has semantic overlap with an existing topic it is trained on. We propose a simple yet effective OOS detection method that outperforms standard OOS detection methods in a real-world deployment of virtual assistants. We discuss the various design and deployment considerations for a cloud platform solution to train virtual assistants and deploy them at scale. Additionally, we propose a collection of datasets that replicates real-world scenarios and show comprehensive results in various settings using both offline and online evaluation metrics.
BibTeX
@inproceedings{qian-etal-2022-distinguish,
title = "Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants",
author = "Qian, Cheng and
Qi, Haode and
Wang, Gengyu and
Kunc, Ladislav and
Potdar, Saloni",
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.51/",
doi = "10.18653/v1/2022.emnlp-industry.51",
pages = "502--511"
}