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:本文介绍我们参加SemEval-2023任务9(多语种Tweets的亲密分析);我们用不同数据增强技术产生的培训数据集和合成数据对一些最受欢迎的变压器模型进行微调;在开发阶段,我们通过使用XLM-T取得最佳结果。数据增强技术使结果略有改善。我们的系统在45个参与系统中排名第27位,尽管其成果不大,但我们的系统在葡萄牙语、英语和荷兰语等语言上显示出有希望的结果。我们的所有代码都可在存储库(https://github.com/isegura/hulat_interacty}中查阅。</s>