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

We propose MINT, a new Multilingual INTimacy analysis dataset covering 13,372
tweets in 10 languages including English, French, Spanish, Italian, Portuguese,
Korean, Dutch, Chinese, Hindi, and Arabic. We benchmarked a list of popular
multilingual pre-trained language models. The dataset is released along with
the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis
(this https URL).

提出了一个名为 MINT 的针对 13,372 条推文的多语言亲密性分析数据集，并在英语、法语、西班牙语、意大利语、葡萄牙语、韩语、荷兰语、中文、印地语和阿拉伯语等 10 种语言上进行了基准测试，并发布了该数据集。同时，与此数据集相关的语义分析比赛任务为 SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis。