{"type":"ScholarlyArticle","title":"An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems","authors":[{"name":"Yiping Song","slug":"yiping-song","given":"Yiping","family":"Song","affiliation":"Institute of Network Computing and Information Systems , School of EECS , Peking University , China"},{"name":"Cheng-Te Li","slug":"cheng-te-li","given":"Cheng-Te","family":"Li","affiliation":"Department of Statistics , National Cheng Kung University , Taiwan"},{"name":"Jian-Yun Nie","slug":"jian-yun-nie","given":"Jian-Yun","family":"Nie","affiliation":"University of Montreal , Canada"},{"name":"Ming Zhang","slug":"ming-zhang","given":"Ming","family":"Zhang","affiliation":"Institute of Network Computing and Information Systems , School of EECS , Peking University , China"},{"name":"Dongyan Zhao","slug":"dongyan-zhao","given":"Dongyan","family":"Zhao","affiliation":"Institute of Computer Science and Technology , Peking University , China"},{"name":"Rui Yan","slug":"rui-yan","given":"Rui","family":"Yan","affiliation":"Institute of Computer Science and Technology , Peking University , China"}],"abstract":"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 novel 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","url":"https://ijcai.org/proceedings/2018/609","pdf":"https://ijcai.org/proceedings/2018/0609.pdf","markdown":"https://ijcai.org/proceedings/2018/609.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/609","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":609,"pages":{"first":4382,"last":4388},"keywords":["Humans and AI: Human-Computer Interaction","Natural Language Processing: Dialogue"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, KyungHyun Cho, and Yoshua Bengio. 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