Neural Multi-task Learning in Automated Assessment
This work addresses the need for more accurate automated assessment tools in education, though it is incremental as it builds on existing multi-task learning approaches.
The paper tackled the problem of improving automated essay scoring by developing a multi-task neural network that jointly optimizes grammatical error detection and essay scoring, showing that error detection significantly enhances scoring performance.
Grammatical error detection and automated essay scoring are two tasks in the area of automated assessment. Traditionally these tasks have been treated independently with different machine learning models and features used for each task. In this paper, we develop a multi-task neural network model that jointly optimises for both tasks, and in particular we show that neural automated essay scoring can be significantly improved. We show that while the essay score provides little evidence to inform grammatical error detection, the essay score is highly influenced by error detection.