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arXiv · 1905.13637

GSN: A Graph-Structured Network for Multi-Party Dialogues

Wenpeng Hu·Zhangming Chan·Bing Liu·Dongyan Zhao·Jinwen Ma·Rui Yan
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Abstract

Existing neural models for dialogue response generation assume that utterances are sequentially organized. However, many real-world dialogues involve multiple interlocutors (i.e., multi-party dialogues), where the assumption does not hold as utterances from different interlocutors can occur "in parallel." This paper generalizes existing sequence-based models to a Graph-Structured neural Network (GSN) for dialogue modeling. The core of GSN is a graph-based encoder that can model the information flow along the graph-structured dialogues (two-party sequential dialogues are a special case). Experimental results show that GSN significantly outperforms existing sequence-based models.

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Wenpeng Hu, Zhangming Chan, Bing Liu, Dongyan Zhao, Jinwen Ma, Rui Yan. 2019-05-31. GSN: A Graph-Structured Network for Multi-Party Dialogues. https://arxiv.org/abs/1905.13637

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