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

• URL: http://arxiv.org/abs/2011.05197v1
• Date: Tue, 10 Nov 2020 15:50:37 GMT
• Title: UmBERTo-MTSA @ AcCompl-It: Improving Complexity and Acceptability Prediction with Multi-task Learning on Self-Supervised Annotations
• Authors: Gabriele Sarti
• Abstract summary: 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. Nerve language models are fine-tuned using this procedure in the context of the AcCompl-it shared task at EVALITA 2020.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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