Dialog as a Vehicle for Lifelong Learning
Dialog as a Vehicle for Lifelong Learning
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发表时间：
2020-06
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通讯作者：
Aishwarya Padmakumar;R. Mooney
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作者：
Aishwarya Padmakumar;R. Mooney
对话系统的研究主要集中在两种主要类型的应用程序-面向任务的对话系统，学习使用澄清，以帮助理解一个目标，和开放式的对话系统，预计将进行无约束的“闲聊”对话。然而，对话交互也可以用于获得各种类型的知识，这些知识可以用于改进底层语言理解系统或对话所作用的其他机器学习系统。在这份立场文件中，我们提出的问题，设计对话系统，使终身学习作为一个重要的挑战性问题，特别是涉及物理位置的机器人的应用程序。我们包括在这方面的工作，并讨论仍然有待解决的挑战的例子。
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.