Machine translation evaluation metrics for quality assessment of automatically simplified sentences

We investigate whether it is possible to automatically evaluate the output of automatic text simplification (ATS) systems by using automatic metrics designed for evaluation of machine translation (MT) outputs. In the first step, we select a set of the most promising metrics based on the Pearson’s correlation coefficients between those metrics and human scores for the overall quality of automatically simplified sentences. Next, we build eight classifiers on the training dataset using the subset of 13 most promising metrics as features, and apply two best classifiers on the test set. Additionally, we apply an attribute selection algorithm to further select best subset of features for our classification experiments. Finally, we report on the success of our systems in the shared task and report on confusion matrices which can help to gain better insights into the most challenging problems of this task