An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems

Yiping Song, Cheng-Te Li, Jian-Yun Nie, Ming Zhang, Dongyan Zhao, Rui Yan
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Human-computer conversation systems have attracted much attention in Natural Language Processing. Conversation systems can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (namely a query ) in a large conversational repository and return a reply that best matches the query. Generative approaches synthesize new replies. Both ways have certain advantages but suffer from their own disadvantages. We propose a
more » ... ensemble of retrieval-based and generation-based conversation system. The retrieved candidates, in addition to the original query, are fed to a reply generator via a neural network, so that the model is aware of more information. The generated reply together with the retrieved ones then participates in a re-ranking process to find the final reply to output. Experimental results show that such an ensemble system outperforms each single module by a large margin.
doi:10.24963/ijcai.2018/609 dblp:conf/ijcai/SongLNZZY18 fatcat:66bct74senavxkuewahbupcofi