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This work describes a self-supervised data augmentation approach used toimprove learning models' performances when only a moderate amount of labeleddata is available. Multiple copies of the original model are initially trainedon the downstream task. Their predictions are then used to annotate a large setof unlabeled examples. Finally, multi-task training is performed on theparallel annotations of the resulting training set, and final scores areobtained by averaging annotator-specific head predictions. Neural languagemodels are fine-tuned using this procedure in the context of the AcCompl-itshared task at EVALITA 2020, obtaining considerable improvements in predictionquality.

UmBERTo-MTSA @ AcCompl-It: Improving Complexity and Acceptability Prediction with Multi-task Learning on Self-Supervised Annotations