Papers with heterogeneous graph-based neural network

2 papers
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations (2022.acl-long)

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Challenge: Experimental results show that HeterMPC outperforms various baseline models for response generation in multi-party conversations.
Approach: They propose a heterogeneous graph-based neural network for response generation in multi-party conversations which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph.
Outcome: The proposed model outperforms baseline models on the Ubuntu Internet Relay Chat (IRC) channel.
Heterogeneous Graph Neural Networks for Extractive Document Summarization (2020.acl-main)

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Challenge: Existing models capture cross-sentence relations with recurrent neural networks, but they are hard to capture sentence-level long-distance dependency.
Approach: They propose a graph-based neural network for extractive summarization which contains semantic nodes apart from sentences.
Outcome: The proposed graph-based neural network is the first to incorporate different types of nodes into it and perform a qualitative analysis.

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