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Intelligent virtual assistants are currently designed to perform tasks orservices explicitly mentioned by users, so multiple related domains or tasksneed to be performed one by one through a long conversation with many explicitintents. Instead, human assistants are capable of reasoning (multiple) implicitintents based on user utterances via commonsense knowledge, reducing complexinteractions and improving practicality. Therefore, this paper proposes aframework of multi-domain dialogue systems, which can automatically inferimplicit intents based on user utterances and then perform zero-shot promptingusing a large pre-trained language model to trigger suitable singletask-oriented bots. The proposed framework is demonstrated effective to realizeimplicit intents and recommend associated bots in a zero-shot manner.

Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning