{"type":"ScholarlyArticle","title":"Joint Representation Learning of Legislator and Legislation for Roll Call Prediction","authors":[{"name":"Yuqiao Yang","slug":"yuqiao-yang","given":"Yuqiao","family":"Yang","affiliation":"School of Data Science , Fudan University , China; School of Information and Communication , Tokyo Institute of Technology , Japan"},{"name":"Xiaoqiang Lin","slug":"xiaoqiang-lin","given":"Xiaoqiang","family":"Lin","affiliation":"School of Data Science , Fudan University , China"},{"name":"Geng Lin","slug":"geng-lin","given":"Geng","family":"Lin","affiliation":"School of Data Science , Fudan University , China"},{"name":"Zengfeng Huang","slug":"zengfeng-huang","given":"Zengfeng","family":"Huang","affiliation":"School of Data Science , Fudan University , China"},{"name":"Changjian Jiang","slug":"changjian-jiang","given":"Changjian","family":"Jiang","affiliation":"School of International Relations & Public Affairs , Fudan University , China"},{"name":"Zhongyu Wei","slug":"zhongyu-wei","given":"Zhongyu","family":"Wei","affiliation":"School of Data Science , Fudan University , China; Research Institute of Intelligent and Complex Systems , Fudan University , China"}],"abstract":"In this paper, we explore to learn representations of legislation and legislator for the prediction of roll call results. The most popular approach for this topic is named the ideal point model that relies on historical voting information for representation learning of legislators. It largely ignores the context information of the legislative data. We, therefore, propose to incorporate context information to learn dense representations for both legislators and legislation. For legislators, we incorporate relations among them via graph convolutional neural networks (GCN) for their representation learning. For legislation, we utilize its narrative description via recurrent neural networks (RNN) for representation learning. In order to align two kinds of representations in the same vector space, we introduce a triplet loss for the joint training. Experimental results on a self-constructed dataset show the effectiveness of our model for roll call results prediction compared to some state-of-the-art baselines.","doi":"10.24963/ijcai.2020/198","url":"https://ijcai.org/proceedings/2020/198","pdf":"https://ijcai.org/proceedings/2020/0198.pdf","markdown":"https://ijcai.org/proceedings/2020/198.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/198","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":"Data Mining","track":"Main track","number":198,"pages":{"first":1424,"last":1430},"keywords":["Natural Language Processing: NLP Applications and Tools","Data Mining: Mining Text, Web, Social Media"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24287/file_24287.m3u8","length_sec":292,"provider":"hls"}],"references":[{"position":1,"raw":"[Belkin et al., 2006] Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. 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