Related papers: Unsupervised Learning of Hierarchical Conversation Structure

Unsupervised Learning of Hierarchical Conversation Structure

URL: http://arxiv.org/abs/2205.12244v1
Date: Tue, 24 May 2022 17:52:34 GMT
Title: Unsupervised Learning of Hierarchical Conversation Structure
Authors: Bo-Ru Lu, Yushi Hu, Hao Cheng, Noah A. Smith, Mari Ostendorf
Abstract summary: 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. The decoded structure is shown to be useful in enhancing neural models of language for three conversation-level understanding tasks.
Score: 50.29889385593043
License: http://creativecommons.org/licenses/by/4.0/
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. Our code and trained models are available at \url{https://github.com/boru-roylu/THETA}.

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