{"type":"ScholarlyArticle","title":"TEC: A Time Evolving Contextual Graph Model for Speaker State Analysis in Political Debates","authors":[{"name":"Ramit Sawhney","slug":"ramit-sawhney","given":"Ramit","family":"Sawhney","affiliation":""},{"name":"Shivam Agarwal","slug":"shivam-agarwal","given":"Shivam","family":"Agarwal","affiliation":""},{"name":"Arnav Wadhwa","slug":"arnav-wadhwa","given":"Arnav","family":"Wadhwa","affiliation":""},{"name":"Rajiv Shah","slug":"rajiv-shah","given":"Rajiv","family":"Shah","affiliation":""}],"abstract":"Political discourses provide a forum for representatives to express their opinions and contribute towards policy making. \r\nAnalyzing these discussions is crucial for recognizing possible delegates and making better voting choices in an independent nation. \r\nA politician's vote on a proposition is usually associated with their past discourses and impacted by cohesion forces in political parties.\r\nWe focus on predicting a speaker's vote on a bill by augmenting linguistic models with temporal and cohesion contexts.\r\nWe propose TEC, a time evolving graph based model that jointly employs links between motions, speakers, and temporal politician states. \r\nTEC outperforms competitive models, illustrating the benefit of temporal and contextual signals for predicting a politician's stance.","doi":"10.24963/ijcai.2021/489","url":"https://ijcai.org/proceedings/2021/489","pdf":"https://ijcai.org/proceedings/2021/0489.pdf","markdown":"https://ijcai.org/proceedings/2021/489.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/489","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning Applications","track":"Main Track","number":489,"pages":{"first":3552,"last":3558},"keywords":["Natural Language Processing: NLP Applications and Tools","Machine Learning Applications: Humanities"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}