基于常识推理的隐式意图预测与推荐零样本提示方法
Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning
AI总结：
针对虚拟助手只能处理显式意图、交互繁琐的问题，本文提出多领域对话框架，利用常识推理推断隐式意图并通过大型预训练语言模型进行零样本提示，以推荐并触发合适的任务型机器人。
摘要：
智能虚拟助手目前被设计为执行用户明确提及的任务或服务，因此多个相关领域或任务需要通过包含许多显式意图的冗长对话逐一完成。相比之下，人类助手能够借助常识知识，根据用户话语推理出（多个）隐式意图，从而减少复杂交互并提升实用性。为此，本文提出一个多领域对话系统框架，该框架可根据用户话语自动推断隐式意图，随后利用大型预训练语言模型执行零样本提示，以触发合适的单一任务型机器人。实验表明，所提出的框架能够有效地以零样本方式实现隐式意图识别并推荐相关机器人。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
