论文标题
通过开放域对话进行学习
Towards Learning Through Open-Domain Dialog
论文作者
论文摘要
能够通过无域限制的对话来学习的人工代理的开发有可能允许机器学习如何以与人类类似的方式执行任务并改变我们与他们的关系。但是，这一领域的研究实际上不存在。在本文中，我们确定对话框系统能够从对话框中学习并提出可用于实现这些修改的通用方法所需的修改。更具体地说，我们讨论如何从对话框中提取知识，用于更新代理的语义网络并以行动和观察为基础。这样，我们希望提高人们对此主题的认识，以便将来成为研究的重点。
The development of artificial agents able to learn through dialog without domain restrictions has the potential to allow machines to learn how to perform tasks in a similar manner to humans and change how we relate to them. However, research in this area is practically nonexistent. In this paper, we identify the modifications required for a dialog system to be able to learn from the dialog and propose generic approaches that can be used to implement those modifications. More specifically, we discuss how knowledge can be extracted from the dialog, used to update the agent's semantic network, and grounded in action and observation. This way, we hope to raise awareness for this subject, so that it can become a focus of research in the future.
