论文标题
umberto-mtsa @ age-it：通过对自我监督注释的多任务学习提高复杂性和可接受性预测
UmBERTo-MTSA @ AcCompl-It: Improving Complexity and Acceptability Prediction with Multi-task Learning on Self-Supervised Annotations
论文作者
论文摘要
这项工作描述了一种自我监督的数据增强方法，用于改善学习模型的性能，仅当可用的标记数据可用时。原始模型的多个副本最初是在下游任务上训练的。然后，他们的预测被用来注释大量未标记的示例。最后，对由此产生的训练集的并行注释进行了多任务训练，并通过平均特定于注释者的头部预测获得最终得分。神经语言模型在2020年的完成任务的背景下使用此过程进行微调，从而获得了可观的预测质量。
This work describes a self-supervised data augmentation approach used to improve learning models' performances when only a moderate amount of labeled data is available. Multiple copies of the original model are initially trained on the downstream task. Their predictions are then used to annotate a large set of unlabeled examples. Finally, multi-task training is performed on the parallel annotations of the resulting training set, and final scores are obtained by averaging annotator-specific head predictions. Neural language models are fine-tuned using this procedure in the context of the AcCompl-it shared task at EVALITA 2020, obtaining considerable improvements in prediction quality.
