{"type":"ScholarlyArticle","title":"Two-stage Behavior Cloning for Spoken Dialogue System in Debt Collection","authors":[{"name":"Zihao Wang","slug":"zihao-wang","given":"Zihao","family":"Wang","affiliation":"Ant Financial Services Group , Hangzhou , China"},{"name":"Jia Liu","slug":"jia-liu","given":"Jia","family":"Liu","affiliation":"Ant Financial Services Group , Hangzhou , China"},{"name":"Hengbin Cui","slug":"hengbin-cui","given":"Hengbin","family":"Cui","affiliation":"Ant Financial Services Group , Hangzhou , China"},{"name":"Chunxiang Jin","slug":"chunxiang-jin","given":"Chunxiang","family":"Jin","affiliation":"Ant Financial Services Group , Hangzhou , China"},{"name":"Minghui Yang","slug":"minghui-yang","given":"Minghui","family":"Yang","affiliation":"Ant Financial Services Group , Hangzhou , China"},{"name":"Yafang Wang","slug":"yafang-wang","given":"Yafang","family":"Wang","affiliation":"Ant Financial Services Group , Hangzhou , China"},{"name":"Xiaolong Li","slug":"xiaolong-li","given":"Xiaolong","family":"Li","affiliation":"Ant Financial Services Group , Hangzhou , China"},{"name":"Renxin Mao","slug":"renxin-mao","given":"Renxin","family":"Mao","affiliation":"Ant Financial Services Group , Hangzhou , China"}],"abstract":"With the rapid growth of internet finance and the booming of financial lending, the intelligent calling for debt collection in FinTech companies has driven increasing attention. Nowadays, the widely used intelligent calling system is based on dialogue flow, namely configuring the interaction flow with the finite-state machine. In our scenario of debt collection, the completed dialogue flow contains more than one thousand interactive paths. All the dialogue procedures are artificially specified, with extremely high maintenance costs and error-prone. To solve this problem, we propose the behavior-cloning-based collection robot framework without any dialogue flow configuration, called two-stage behavior cloning (TSBC). In the first stage, we use multi-label classification model to obtain policies that may be able to cope with the current situation according to the dialogue state; in the second stage, we score several scripts under each obtained policy to select the script with the highest score as the reply for the current state. This framework makes full use of the massive manual collection records without labeling and fully absorbs artificial wisdom and experience. We have conducted extensive experiments in both single-round and multi-round scenarios and showed the effectiveness of the proposed system. The accuracy of a single round of dialogue can be improved by 5%, and the accuracy of multiple rounds of dialogue can be increased by 3.1%.","doi":"10.24963/ijcai.2020/639","url":"https://ijcai.org/proceedings/2020/639","pdf":"https://ijcai.org/proceedings/2020/0639.pdf","markdown":"https://ijcai.org/proceedings/2020/639.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/639","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special Track on AI in FinTech","track":"Special Track on AI in FinTech","number":639,"pages":{"first":4633,"last":4639},"keywords":["Foundation for AI in FinTech: Deep learning and representation for FinTech","AI for banking: AI for smarter banking services","Foundation for AI in FinTech: Automated financial systems and services","Foundation for AI in FinTech: Analyzing big financial data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24520/file_24520.m3u8","length_sec":302,"provider":"hls"}],"references":[{"position":1,"raw":"[Bahl et al., 1986] Lalit R Bahl, Peter F Brown, Peter V De Souza, and Robert L Mercer. 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