We demonstrate how supervised discriminative machine learning techniques can be used to automate the assessment of ‘English as a Second or Other Language’ (ESOL) examination scripts. In particular, we use rank preference learning to explicitly model the grade relationships between scripts. A number of different features are extracted and ablation tests are used to investigate their contribution to overall performance. A comparison between regression and rank preference models further supports our method. .
