论文标题
零射击提示与常识性推理的隐性意图预测和建议
Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning
论文作者
论文摘要
智能虚拟助手目前旨在执行用户明确提及的任务或服务，因此需要通过长期对话，并与许多明确的意图一起进行多个相关的域或任务。取而代之的是，人类助手能够通过常识性知识来基于用户话语，减少复杂的互动并改善实用性来推理（多个）隐式意图。因此，本文提出了一个多域对话系统的框架，该框架可以根据用户话语自动推断隐式意图，然后使用大型预训练的语言模型进行零射击，以触发合适的单一任务型机器人。所提出的框架被证明有效地实现了隐性意图，并以零拍的方式推荐相关的机器人。
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.
