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arXiv · 2205.12244

Unsupervised Learning of Hierarchical Conversation Structure

Bo-Ru Lu·Yushi Hu·Hao Cheng·Noah A. Smith·Mari Ostendorf
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Abstract

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.

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Bo-Ru Lu, Yushi Hu, Hao Cheng, Noah A. Smith, Mari Ostendorf. 2022-05-24. Unsupervised Learning of Hierarchical Conversation Structure. https://arxiv.org/abs/2205.12244

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