This 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 \url{https://github.com/isegura/hulat_intimacy}.

翻译：本文描述了我们在SemEval-2023任务9（多语言推文亲密度分析）中的参与情况。我们利用训练数据集以及通过不同数据增强技术生成的合成数据，对若干主流Transformer模型进行了微调。在开发阶段，我们使用XLM-T取得了最佳结果。数据增强技术对结果的提升效果极为有限。我们的系统在45个参赛系统中位列第27位。尽管成绩平平，但该系统在葡萄牙语、英语和荷兰语等语言中展现出良好的应用前景。所有代码均可在以下仓库获取：\url{https://github.com/isegura/hulat_intimacy}。