Recently, various response generation models for two-party conversations have
achieved impressive improvements, but less effort has been paid to multi-party
conversations (MPCs) which are more practical and complicated. Compared with a
two-party conversation where a dialogue context is a sequence of utterances,
building a response generation model for MPCs is more challenging, since there
exist complicated context structures and the generated responses heavily rely
on both interlocutors (i.e., speaker and addressee) and history utterances. To
address these challenges, we present HeterMPC, a heterogeneous graph-based
neural network for response generation in MPCs which models the semantics of
utterances and interlocutors simultaneously with two types of nodes in a graph.
Besides, we also design six types of meta relations with
node-edge-type-dependent parameters to characterize the heterogeneous
interactions within the graph. Through multi-hop updating, HeterMPC can
adequately utilize the structural knowledge of conversations for response
generation. Experimental results on the Ubuntu Internet Relay Chat (IRC)
channel benchmark show that HeterMPC outperforms various baseline models for
response generation in MPCs.

提出了一种基于异构图神经网络的多方会话响应生成模型，通过建模话语和交互者的语义信息及设计不同类型的元关系来处理多方对话中的结构信息，实验证明其在 Ubuntu Internet Relay Chat（IRC）通道基准测试上优于其他基线模型。