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Unsupervised Learning of Hierarchical Conversation Structure. (arXiv:2205.12244v1 [cs.CL])
cs.CL updates on arXiv.org arxiv.org
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 …
arxiv conversation hierarchical learning unsupervised unsupervised learning