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.

翻译：智能虚拟助手当前仅能执行用户明确提及的任务或服务，因此需要通过包含大量显式意图的长对话逐一完成多个关联领域或任务。而人类助手能够基于用户话语通过常识知识推理（多个）隐式意图，从而减少复杂交互并提升实用性。为此，本文提出一种多领域对话系统框架，该框架可基于用户话语自动推断隐式意图，并利用大型预训练语言模型进行零样本提示以触发合适的单任务导向型智能体。实验证明，所提框架能够有效实现隐式意图识别并以零样本方式推荐关联智能体。