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.

本文提出了一种简单且有效的 OOS 检测方法，它在实际场景中的虚拟助手部署中优于标准方法，并讨论了云平台解决方案的各种设计和部署考虑因素。同时，我们提出了一组数据集，模拟实际情况，并使用离线和在线评估指标在不同场景下展示了全面结果。