Argument Mining for Improving the Automated Scoring of Persuasive Essays


  • Huy Nguyen University of Pittsburgh
  • Diane Litman University of Pittsburgh



argument mining, automated essay scoring


End-to-end argument mining has enabled the development of new automated essay scoring (AES) systems that use argumentative features (e.g., number of claims, number of support relations) in addition to traditional legacy features (e.g., grammar, discourse structure) when scoring persuasive essays. While prior research has proposed different argumentative features as well as empirically demonstrated their utility for AES, these studies have all had important limitations. In this paper we identify a set of desiderata for evaluating the use of argument mining for AES, introduce an end-to-end argument mining system and associated argumentative feature sets, and present the results of several studies that both satisfy the desiderata and demonstrate the value-added of argument mining for scoring persuasive essays.




How to Cite

Nguyen, H., & Litman, D. (2018). Argument Mining for Improving the Automated Scoring of Persuasive Essays. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).