  Intelligent virtual assistants are currently designed to perform tasks or
services explicitly mentioned by users, so multiple related domains or tasks
need to be performed one by one through a long conversation with many explicit
intents. Instead, human assistants are capable of reasoning (multiple) implicit
intents based on user utterances via commonsense knowledge, reducing complex
interactions and improving practicality. Therefore, this paper proposes a
framework of multi-domain dialogue systems, which can automatically infer
implicit intents based on user utterances and then perform zero-shot prompting
using a large pre-trained language model to trigger suitable single
task-oriented bots. The proposed framework is demonstrated effective to realize
implicit intents and recommend associated bots in a zero-shot manner.
