The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, not withstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Fooling Automatic Short Answer Grading Systems

Author:Anna Filighera, Tim Steuer, Christoph Rensing
Date:July 2020
Kind:In proceedings - use for conference & workshop papers
Publisher:Springer International Publishing
Book title:Artificial Intelligence in Education
Editor:41 Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (Eds.)
Volume:Part I
Keywords:Automatic Short Answer Grading, Adversarial Attacks, Automatic Assessment
Research Area(s):Knowledge Media
Abstract:With the rising success of adversarial attacks on many NLP tasks, systems which actually operate in an adversarial scenario need to be reevaluated. For this purpose, we pose the following research question: How difficult is it to fool automatic short answer grading systems? In particular, we investigate the robustness of the state of the art automatic short answer grading system proposed by Sung et al. towards cheating in the form of universal adversarial trigger employment. These are short token sequences that can be prepended to students' answers in an exam to artificially improve their automatically assigned grade. Such triggers are especially critical as they can easily be used by anyone once they are found. In our experiments, we discovered triggers which allow students to pass exams with passing thresholds of 50% without answering a single question correctly. Furthermore, we show that such triggers generalize across models and datasets in this scenario, nullifying the defense strategy of keeping grading models or data secret.
Full paper (pdf)

[Export this entry to BibTeX]