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
https://github.com/isegura/hulat_intimacy.

该研究讨论了使用数据增强技术 fine-tune 最流行的 Transformer 模型来参加 SemEval-2023 任务 9，多语言推文的亲密度分析。研究结果表明，我们的系统在多种语言中有良好表现，尤其是葡萄牙语、英语和荷兰语。

HULAT 参加 SemEval-2023 任务 9：基于数据增强的预训练 Transformer 在多语言推特亲密度分析中的应用

HULAT at SemEval-2023 Task 9: Data augmentation for pre-trained transformers applied to Multilingual Tweet Intimacy Analysis

The usage of part-of-day nouns, such as 'night', and their time-specific
greetings ('good night'), varies across languages and cultures. We show the
possibilities that Twitter offers for studying the semantics of these terms and
its variability between countries. We mine a worldwide sample of multilingual
tweets with temporal greetings, and study how their frequencies vary in
relation with local time. The results provide insights into the semantics of
these temporal expressions and the cultural and sociological factors
influencing their usage.

本文探讨了 Twitter 作为研究跨语言文化中时间表达习惯的可能性，研究了多语种推文中关于时间问候的频率和其与当地时间的关系，以揭示时间表达习惯的语义和文化社会因素。