论文标题
无监督的层次对话结构学习
Unsupervised Learning of Hierarchical Conversation Structure
论文作者
论文摘要
人类的对话可以以许多不同的方式发展，从而为自动理解和摘要带来挑战。面向目标的对话通常具有有意义的次数结构，但它可能是高度依赖的。这项工作介绍了一种无监督的方法，用于学习层次的对话结构，包括转弯和子女图段标签，分别与对话行为和子任务大致相对应。解码结构被证明可用于增强语言的神经模型，用于三个对话级别理解任务。此外，可以通过自动汇总来解释有限的有限国家子女度用户网络。
Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dialogue structure, but it can be highly domain-dependent. This work introduces an unsupervised approach to learning hierarchical conversation structure, including turn and sub-dialogue segment labels, corresponding roughly to dialogue acts and sub-tasks, respectively. The decoded structure is shown to be useful in enhancing neural models of language for three conversation-level understanding tasks. Further, the learned finite-state sub-dialogue network is made interpretable through automatic summarization.
