Neural Multi-task Learning in Automated Assessment
Table 1: Mapping of FCE   to essay scores
['Exam Score', 'Essay Score']
In particular, we modified the neural network such that the output of the hidden states in each direction are concatenated and averaged over all time-steps before being fed into the AES output layer. The AES output layer maps this concatenated vector to a single value, feeds this value into a sigmoid function and scales it appropriately. The AES loss used is the squared error between the predicted score and the gold standard essay score. We use the public FCE dataset for our experiments. The dataset contains short essays written by ESL learners for the First Certificate in English (FCE) (an upper intermediate level exam) To our knowledge, this is the only public dataset that has annotations for both GED and AES on the same texts. The dataset contains 1244 scripts where each script contains two essays. Our final training, development, and test set contains 2059, 198, and 194 essays respectively.