HULAT at SemEval-2023 Task 9: Data Augmentation for Pre-trained Transformers Applied to Multilingual Tweet Intimacy Analysis

AbstractThis paper describes our participation in SemEval-2023 Task 9, Intimacy Analysis of Multilingual Tweets. We fine-tune some of the most popular transformer models with the training dataset and synthetic data generated by different data augmentation techniques. During the development phase, our best results were obtained by using XLM-T. Data augmentation techniques provide a very slight improvement in the results. Our system ranked in the 27th position out of the 45 participating systems. Despite its modest results, our system shows promising results in languages such as Portuguese, English, and Dutch. All our code is available in the repository https://github.com/isegura/hulat_intimacy.