论文标题
对话策略学习联合澄清和主动学习查询
Dialog Policy Learning for Joint Clarification and Active Learning Queries
论文作者
论文摘要
智能系统需要能够从错误，解决不确定性并适应训练中未见的新颖概念中恢复。对话框的交互可以通过使用澄清来纠正和解决不确定性以及积极学习查询来学习操作过程中遇到的新概念。对话系统的先前工作要么专注于专门学习如何执行澄清/信息寻求或进行积极学习。在这项工作中，我们培训层次对话策略，以在线购物应用程序的基于互动语言的图像检索任务的背景下共同执行澄清和积极学习，并证明，与其中一项或一项功能的静态对话策略相比，共同学习澄清和主动学习的对话策略更有效。
Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training. Dialog interaction can enable this by the use of clarifications for correction and resolving uncertainty, and active learning queries to learn new concepts encountered during operation. Prior work on dialog systems has either focused on exclusively learning how to perform clarification/ information seeking, or to perform active learning. In this work, we train a hierarchical dialog policy to jointly perform both clarification and active learning in the context of an interactive language-based image retrieval task motivated by an online shopping application, and demonstrate that jointly learning dialog policies for clarification and active learning is more effective than the use of static dialog policies for one or both of these functions.
