Discovering Dialog Structure Graph for Open-Domain Dialog Generation
Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che,, Ting Liu

TL;DR
This paper introduces DVAE-GNN, a novel unsupervised method to discover interpretable dialog structure graphs from human conversations, enhancing open-domain dialog generation by providing structured background knowledge.
Contribution
The paper proposes a Discrete Variational Auto-Encoder with Graph Neural Network to discover a unified, human-readable dialog structure graph, integrating session and utterance semantics for improved dialog modeling.
Findings
DVAE-GNN effectively discovers meaningful dialog structures.
The dialog structure graph improves multi-turn dialog coherence.
Experimental results outperform baseline models on benchmark datasets.
Abstract
Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation. In this paper, we conduct unsupervised discovery of dialog structure from chitchat corpora, and then leverage 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. The structure is a two-layer directed graph that contains session-level semantics in the upper-layer vertices, utterance-level semantics in the lower-layer vertices, and edges among these semantic vertices. In particular, we integrate GNN into DVAE to fine-tune utterance-level semantics for more effective recognition of session-level semantic vertex. Furthermore, to alleviate the…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Speech and dialogue systems
MethodsGraph Neural Network
