Jamil Hasan
Department of Computer Science, University of Idaho, 875 Perimeter Drive, Moscow, 83844, ID, USA.
Res Sq. 2023 Jul 13:rs.3.rs-3129882. doi: 10.21203/rs.3.rs-3129882/v1.
In conversational query answering systems, context plays a significant role in accurately and meaningfully carrying it forward. In many chatbots, such as in Expedia, the discussion quickly degenerates into circling back to restarting the conversation or to inviting a live agent to intervene because the bot could not grasp the context. Contexts shorten interactions by way of implied query constraints to narrow search and to not repeat them in subsequent queries. In this paper, we introduce a novel way of viewing contexts as a distance function via the concept of query relaxation. We demonstrate that a typed domain distance function is sufficient to model context in a conversation. Our approach is based on the idea of non-monotonic constraint inheritance in a context hierarchy.
在对话式问答系统中,上下文在准确且有意义地推进对话方面起着重要作用。在许多聊天机器人中,比如在Expedia中,由于机器人无法理解上下文,讨论很快就会退化为循环回到重新开始对话或邀请在线客服介入。上下文通过隐含的查询约束来缩短交互,以缩小搜索范围并避免在后续查询中重复。在本文中,我们通过查询松弛的概念引入了一种将上下文视为距离函数的新颖方式。我们证明了一个类型化的领域距离函数足以对对话中的上下文进行建模。我们的方法基于上下文层次结构中非单调约束继承的思想。