Show simple item record

dc.contributor.advisorNg, Vincent
dc.creatorKe, Zixuan
dc.date.accessioned2019-10-09T20:12:47Z
dc.date.available2019-10-09T20:12:47Z
dc.date.created2019-05
dc.date.issued2019-05
dc.date.submittedMay 2019
dc.identifier.urihttps://hdl.handle.net/10735.1/6987
dc.description.abstractDespite being investigated for over 50 years, the task of Automated Essay Scoring (AES) is far from being solved. Nevertheless, it continues to draw a lot of attention in the natural language processing community in part because of its commercial and educational values as well as the research challenges it brings about. While the majority of work on AES has focused on evaluating an essay’s overall quality, comparatively less work has focused on scoring an essay along specific dimensions of quality. Among many dimensions, argument persuasiveness is arguably the most important but largely ignored dimension. In this thesis, we focus on argument persuasiveness scoring. First, we present our publicly available corpus for argument persuasiveness. Our corpus is the first corpus of essays that are simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument’s persuasiveness. The inter-annotator agreement and the oracle experiments indicate that our annotations are reliable and the attributes can help predict the persuasiveness score. Second, we present 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. The evaluation of our models shows that automatically computed attributes are useful for persuasiveness scoring and that performance can be improved by improving attribute prediction.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rights©2019 Zixuan Ke
dc.subjectNatural language processing (Computer science)
dc.subjectGrading and marking (Students)
dc.subjectDissertations, Academic
dc.subjectPersuasion (Rhetoric)
dc.titleAutomated Essay Scoring: Argument Persuasiveness
dc.typeThesis
dc.date.updated2019-10-09T20:12:47Z
dc.type.materialtext
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.departmentComputer Science
thesis.degree.levelMasters
thesis.degree.nameMSCS


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record