  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.
