  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.
