Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays

TL;DR

This paper introduces a multi-task learning approach using BiLSTMs for automatic essay scoring, effectively evaluating both overall scores and individual traits, outperforming single-task models.

Contribution

It presents a novel multi-task learning framework that improves holistic essay scoring and trait evaluation compared to traditional single-task methods.

Findings

MTL-based BiLSTM achieves best holistic scoring results.

Multi-task approach performs well on trait scoring.

Ablation tests identify most effective essay traits.

Abstract

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.