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Dialog as a Vehicle for Lifelong Learning
arXiv - CS - Computation and Language Pub Date : 2020-06-26 , DOI: arxiv-2006.14767
Aishwarya Padmakumar, Raymond J. Mooney

Dialog systems research has primarily been focused around two main types of applications - task-oriented dialog systems that learn to use clarification to aid in understanding a goal, and open-ended dialog systems that are expected to carry out unconstrained "chit chat" conversations. However, dialog interactions can also be used to obtain various types of knowledge that can be used to improve an underlying language understanding system, or other machine learning systems that the dialog acts over. In this position paper, we present the problem of designing dialog systems that enable lifelong learning as an important challenge problem, in particular for applications involving physically situated robots. We include examples of prior work in this direction, and discuss challenges that remain to be addressed.

中文翻译:

对话作为终身学习的载体

对话系统的研究主要集中在两种主要类型的应用程序上 - 学习使用澄清来帮助理解目标的面向任务的对话系统,以及预期进行不受约束的“闲聊”对话的开放式对话系统。然而,对话交互也可用于获取各种类型的知识,这些知识可用于改进底层语言理解系统或对话作用的其他机器学习系统。在这份立场文件中,我们提出了设计对话系统的问题,使终身学习成为一个重要的挑战问题,特别是对于涉及物理位置机器人的应用。我们包括了在这个方向上的先前工作的例子,并讨论了仍有待解决的挑战。
更新日期:2020-06-29
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