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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.</abstract>
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%0 Conference Proceedings
%T Unsupervised Learning of Hierarchical Conversation Structure
%A Lu, Bo-Ru
%A Hu, Yushi
%A Cheng, Hao
%A Smith, Noah A.
%A Ostendorf, Mari
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F lu-etal-2022-unsupervised
%X 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.
%R 10.18653/v1/2022.findings-emnlp.415
%U https://aclanthology.org/2022.findings-emnlp.415
%U https://doi.org/10.18653/v1/2022.findings-emnlp.415
%P 5657-5670

Markdown (Informal)

[Unsupervised Learning of Hierarchical Conversation Structure](https://aclanthology.org/2022.findings-emnlp.415) (Lu et al., Findings 2022)

• Unsupervised Learning of Hierarchical Conversation Structure (Lu et al., Findings 2022)

ACL

• Bo-Ru Lu, Yushi Hu, Hao Cheng, Noah A. Smith, and Mari Ostendorf. 2022. Unsupervised Learning of Hierarchical Conversation Structure. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5657–5670, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.