@inproceedings{lu2022unsupervised,
author = {Lu, Bo-Ru and Hu, Yushi and Cheng, Hao and A. Smith, Noah and Ostendorf, Mari},
title = {Unsupervised Learning of Hierarchical Conversation Structure},
booktitle = {EMNLP 2022},
year = {2022},
month = {November},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/unsupervised-learning-of-hierarchical-conversation-structure/},
}