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Learning interpretable dialog structure from human-human dialogs yields basicinsights into the structure of conversation, and also provides backgroundknowledge to facilitate dialog generation. In this paper, we conductunsupervised discovery of dialog structure from chitchat corpora, and thenleverage it to facilitate dialog generation in downstream systems. To this end,we present a Discrete Variational Auto-Encoder with Graph Neural Network(DVAE-GNN), to discover a unified human-readable dialog structure. Thestructure is a two-layer directed graph that contains session-level semanticsin the upper-layer vertices, utterance-level semantics in the lower-layervertices, and edges among these semantic vertices. In particular, we integrateGNN into DVAE to fine-tune utterance-level semantics for more effectiverecognition of session-level semantic vertex. Furthermore, to alleviate thedifficulty of discovering a large number of utterance-level semantics, wedesign a coupling mechanism that binds each utterance-level semantic vertexwith a distinct phrase to provide prior semantics. Experimental results on twobenchmark corpora confirm that DVAE-GNN can discover meaningful dialogstructure, and the use of dialog structure graph as background knowledge canfacilitate a graph grounded conversational system to conduct coherentmulti-turn dialog generation.

Discovering Dialog Structure Graph for Open-Domain Dialog Generation