Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants

TL;DR

This paper introduces an effective out-of-scope detection method for virtual assistants, improving their ability to handle unfamiliar queries with semantic overlap, and discusses deployment considerations and datasets for real-world applications.

Contribution

It presents a novel OOS detection approach that outperforms existing methods and provides datasets and deployment insights for scalable virtual assistant solutions.

Findings

Outperforms standard OOS detection methods in real-world scenarios

Provides datasets that replicate real-world OOS detection challenges

Demonstrates effectiveness through offline and online evaluations

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.