MTLHealth: A Deep Learning System for Detecting Disturbing Content in Student Essays
This addresses the need for robust computer systems to support human graders in identifying potential dangers for students, though it appears incremental as it builds on existing computational linguistics advances.
The paper tackles the problem of detecting disturbing content like bullying and self-harm in student essays for standardized tests, resulting in a deep learning system called MTLHealth that uses pre-trained Transformer models to automatically flag such content.
Essay submissions to standardized tests like the ACT occasionally include references to bullying, self-harm, violence, and other forms of disturbing content. Graders must take great care to identify cases like these and decide whether to alert authorities on behalf of students who may be in danger. There is a growing need for robust computer systems to support human decision-makers by automatically flagging potential instances of disturbing content. This paper describes MTLHealth, a disturbing content detection pipeline built around recent advances from computational linguistics, particularly pre-trained language model Transformer networks.