Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants
This addresses a practical challenge for developers of conversational AI systems, though it appears incremental as it builds on existing OOS detection methods.
The paper tackles the problem of out-of-scope detection in virtual assistants, where queries are semantically similar to known topics but outside the chatbot's capabilities, and proposes a method that outperforms standard approaches in real-world deployment.
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.