{"type":"ScholarlyArticle","title":"Adaboost with Auto-Evaluation for Conversational Models","authors":[{"name":"Juncen Li","slug":"juncen-li","given":"Juncen","family":"Li","affiliation":"WeChat Search Application Department , Tencent , China"},{"name":"Ping Luo","slug":"ping-luo","given":"Ping","family":"Luo","affiliation":"Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) , Institute of Computing Technology , CAS , Beijing 100190 , China; University of Chinese Academy of Sciences , Beijing 100049 , China"},{"name":"Ganbin Zhou","slug":"ganbin-zhou","given":"Ganbin","family":"Zhou","affiliation":"Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) , Institute of Computing Technology , CAS , Beijing 100190 , China; University of Chinese Academy of Sciences , Beijing 100049 , China"},{"name":"Fen Lin","slug":"fen-lin","given":"Fen","family":"Lin","affiliation":"WeChat Search Application Department , Tencent , China"},{"name":"Cheng Niu","slug":"cheng-niu","given":"Cheng","family":"Niu","affiliation":"WeChat Search Application Department , Tencent , China"}],"abstract":"We propose a boosting method for conversational models to encourage them to generate more human-like dialogs. In our method, we consider existing conversational models as weak generators and apply Adaboost to update those models. However, conventional Adaboost cannot be directly applied on conversational models. Because for conversational models, conventional Adaboost cannot adaptively adjust the weight on the instance for subsequent learning, result from the simple comparison between the true output y (to an input x) and its corresponding predicted output y' cannot directly evaluate the learning performance on x. To address this issue, we develop the Adaboost with Auto-Evaluation (called AwE). In AwE, an auto-evaluator is proposed to evaluate the predicted results, which makes it applicable to conversational models. Furthermore, we present the theoretical analysis that the training error drops exponentially fast only if certain assumption over the proposed auto-evaluator holds. Finally, we empirically show that AwE visibly boosts the performance of existing single conversational models and also outperforms the other ensemble methods for conversational models.","doi":"10.24963/ijcai.2018/580","url":"https://ijcai.org/proceedings/2018/580","pdf":"https://ijcai.org/proceedings/2018/0580.pdf","markdown":"https://ijcai.org/proceedings/2018/580.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/580","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":580,"pages":{"first":4173,"last":4179},"keywords":["Natural Language Processing: Dialogue","Natural Language Processing: Natural Language Generation"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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