Learning to Give Feedback: Modeling Attributes Affecting Argument Persuasiveness in Student Essays

Zixuan Ke, Winston Carlile, Nishant Gurrapadi and Vincent Ng.
Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 4130--4136, 2018.

Click here for the PDF version. The talk slides are available here.

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 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.

BibTeX entry

@InProceedings{Ke+etal:18a,
  author = {Zixuan Ke and Winston Carlile and Nishant Gurrapadi and Vincent Ng},
  title = {Learning to Give Feedback: Modeling Attributes Affecting Argument Persuasiveness in Student Essays},
  booktitle = {Proceedings of the 27th International Joint Conference on Artificial Intelligence},
  pages = {4130--4136}, 
  year = 2018}