@inproceedings{zhang-etal-2020-grounded,
title = "Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs",
author = "Zhang, Houyu and
Liu, Zhenghao and
Xiong, Chenyan and
Liu, Zhiyuan",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.184",
doi = "10.18653/v1/2020.acl-main.184",
pages = "2031--2043",
abstract = "Human conversations naturally evolve around related concepts and hop to distant concepts. This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowledge graphs to explicitly model conversation flows. By grounding conversations to the concept space, ConceptFlow represents the potential conversation flow as traverses in the concept space along commonsense relations. The traverse is guided by graph attentions in the concept graph, moving towards more meaningful directions in the concept space, in order to generate more semantic and informative responses. Experiments on Reddit conversations demonstrate ConceptFlow{'}s effectiveness over previous knowledge-aware conversation models and GPT-2 based models while using 70{\%} fewer parameters, confirming the advantage of explicit modeling conversation structures. All source codes of this work are available at \url{https://github.com/thunlp/ConceptFlow}.",
}
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<abstract>Human conversations naturally evolve around related concepts and hop to distant concepts. This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowledge graphs to explicitly model conversation flows. By grounding conversations to the concept space, ConceptFlow represents the potential conversation flow as traverses in the concept space along commonsense relations. The traverse is guided by graph attentions in the concept graph, moving towards more meaningful directions in the concept space, in order to generate more semantic and informative responses. Experiments on Reddit conversations demonstrate ConceptFlow’s effectiveness over previous knowledge-aware conversation models and GPT-2 based models while using 70% fewer parameters, confirming the advantage of explicit modeling conversation structures. All source codes of this work are available at https://github.com/thunlp/ConceptFlow.</abstract>
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%0 Conference Proceedings
%T Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs
%A Zhang, Houyu
%A Liu, Zhenghao
%A Xiong, Chenyan
%A Liu, Zhiyuan
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-grounded
%X Human conversations naturally evolve around related concepts and hop to distant concepts. This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowledge graphs to explicitly model conversation flows. By grounding conversations to the concept space, ConceptFlow represents the potential conversation flow as traverses in the concept space along commonsense relations. The traverse is guided by graph attentions in the concept graph, moving towards more meaningful directions in the concept space, in order to generate more semantic and informative responses. Experiments on Reddit conversations demonstrate ConceptFlow’s effectiveness over previous knowledge-aware conversation models and GPT-2 based models while using 70% fewer parameters, confirming the advantage of explicit modeling conversation structures. All source codes of this work are available at https://github.com/thunlp/ConceptFlow.
%R 10.18653/v1/2020.acl-main.184
%U https://aclanthology.org/2020.acl-main.184
%U https://doi.org/10.18653/v1/2020.acl-main.184
%P 2031-2043
Markdown (Informal)
[Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs](https://aclanthology.org/2020.acl-main.184) (Zhang et al., ACL 2020)
ACL