NUS-IDS at PragTag-2023: Improving Pragmatic Tagging of Peer Reviews through Unlabeled Data

This is the authors' abstract. We don't add key points for this paper.

We describe our models for the Pragmatic Tagging of Peer Reviews Shared Task at the 10th Workshop on Argument Mining at EMNLP-2023. We trained multiple sentence classification models for the above competition task by employing various state-of-the-art transformer models that can be fine-tuned either in the traditional way or through instruction-based fine-tuning. Multiple model predictions on unlabeled data are combined to tentatively label unlabeled instances and augment the dataset to further improve performance on the prediction task. In particular, on the F1000RD corpus, we perform on-par with models trained on 100% of the training data while using only 10% of the data. Overall, on the competition datasets, we rank among the top-2 performers for the different data conditions.