论文标题
通过合成的开放域对话框增强任务机器人参与
Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog
论文作者
论文摘要
为不同类型的对话构建对话系统，例如以任务为导向的对话框（TOD）和开放域对话框（奇数）。为了更好地模仿通常融合各种对话模式的人类级对话，必须建立一个可以有效处理TOD和奇数并访问不同知识源的系统。为了解决缺乏融合任务的可用数据，我们为自动生成对话的框架，以在各种设置中结合了知识的赔率和TOD。此外，我们引入了一个统一的模型PivotBot，该模型能够适当地采用TOD和奇数模式，并访问不同的知识源以有效地处理融合任务。评估结果证明了所提出的模型在TOD和奇数任务之间无缝切换的出色能力。
Many efforts have been made to construct dialog systems for different types of conversations, such as task-oriented dialog (TOD) and open-domain dialog (ODD). To better mimic human-level conversations that usually fuse various dialog modes, it is essential to build a system that can effectively handle both TOD and ODD and access different knowledge sources. To address the lack of available data for the fused task, we propose a framework for automatically generating dialogues that combine knowledge-grounded ODDs and TODs in various settings. Additionally, we introduce a unified model PivotBot that is capable of appropriately adopting TOD and ODD modes and accessing different knowledge sources in order to effectively tackle the fused task. Evaluation results demonstrate the superior ability of the proposed model to switch seamlessly between TOD and ODD tasks.
