Neural Multi-task Learning in Automated Assessment
Table 3: Performance of BiLSTM AES task on public-FCE test set with and without multi-task training objectives (cumulatively) for entire essay context. * indicates that the results are statistically significant when compared to + Elm
['Automated Essay Scoring Cost', 'Automated Essay Scoring Spearman', 'Automated Essay Scoring QWK']
In particular, we see that the semi-supervised language modelling objective (Elm) improves the performance of the model. However, more strikingly we see that when the GED objective is added it increases the performance of our model on the AES task substantially. This is an encouraging result as it means that a neural model can utilise multiple signals in an end-to-end manner to improve AES.