Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays
This work addresses the problem of automated essay scoring for educational assessment, presenting an incremental improvement over existing methods.
The paper tackles automatic essay grading by using a multi-task learning approach that scores essays holistically as the primary task and individual essay traits as auxiliary tasks, finding that a BiLSTM-based system achieves the best holistic scoring results with improved performance on traits.
Most research in the area of automatic essay grading (AEG) is geared towards scoring the essay holistically while there has also been some work done on scoring individual essay traits. In this paper, we describe a way to score essays holistically using a multi-task learning (MTL) approach, where scoring the essay holistically is the primary task, and scoring the essay traits is the auxiliary task. We compare our results with a single-task learning (STL) approach, using both LSTMs and BiLSTMs. We also compare our results of the auxiliary task with such tasks done in other AEG systems. To find out which traits work best for different types of essays, we conduct ablation tests for each of the essay traits. We also report the runtime and number of training parameters for each system. We find that MTL-based BiLSTM system gives the best results for scoring the essay holistically, as well as performing well on scoring the essay traits.