{"type":"ScholarlyArticle","title":"Learning to Give Feedback: Modeling Attributes Affecting Argument Persuasiveness in Student Essays","authors":[{"name":"Zixuan Ke","slug":"zixuan-ke","given":"Zixuan","family":"Ke","affiliation":"Human Language Technology Research Institute , University of Texas at Dallas Richardson , TX 75083-0688"},{"name":"Winston Carlile","slug":"winston-carlile","given":"Winston","family":"Carlile","affiliation":"Human Language Technology Research Institute , University of Texas at Dallas Richardson , TX 75083-0688"},{"name":"Nishant Gurrapadi","slug":"nishant-gurrapadi","given":"Nishant","family":"Gurrapadi","affiliation":"Human Language Technology Research Institute , University of Texas at Dallas Richardson , TX 75083-0688"},{"name":"Vincent Ng","slug":"vincent-ng","given":"Vincent","family":"Ng","affiliation":"Human Language Technology Research Institute , University of Texas at Dallas Richardson , TX 75083-0688"}],"abstract":"Argument persuasiveness is one of the most important dimensions of argumentative essay quality, yet it is little studied in automated essay scoring research. Using a recently released corpus of essays that are simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument’s persuasiveness, we design and train the first set of neural models that predict the persuasiveness of an argument and its attributes in a student essay, enabling useful feedback to be provided to students on why their arguments are (un)persuasive in addition to how persuasive they are.","doi":"10.24963/ijcai.2018/574","url":"https://ijcai.org/proceedings/2018/574","pdf":"https://ijcai.org/proceedings/2018/0574.pdf","markdown":"https://ijcai.org/proceedings/2018/574.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/574","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":574,"pages":{"first":4130,"last":4136},"keywords":["Natural Language Processing: NLP Applications and Tools"],"videos":[],"references":[{"position":1,"raw":"[Al Khatib et al., 2016] Khalid Al Khatib, Henning Wachsmuth, Johannes Kiesel, Matthias Hagen, and Benno Stein. 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