HeterMPC: A Heterogeneous Graph Neural Network for Response Generation
in Multi-Party Conversations
- URL: http://arxiv.org/abs/2203.08500v1
- Date: Wed, 16 Mar 2022 09:50:32 GMT
- Title: HeterMPC: A Heterogeneous Graph Neural Network for Response Generation
in Multi-Party Conversations
- Authors: Jia-Chen Gu, Chao-Hong Tan, Chongyang Tao, Zhen-Hua Ling, Huang Hu,
Xiubo Geng, Daxin Jiang
- Abstract summary: We present HeterMPC, a graph-based neural network for response generation in multi-party conversations (MPCs)
HeterMPC models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph.
Through multi-hop updating, HeterMPC can adequately utilize the structural knowledge of conversations for response generation.
- Score: 76.64792382097724
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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