This paper describes our submission to the SemEval 2023 multilingual tweet intimacy analysis shared task. The goal of the task was to assess the level of intimacy of Twitter posts in ten languages. The proposed approach consists of several steps. First, we perform in-domain pre-training to create a language model adapted to Twitter data. In the next step, we train an ensemble of regression models to expand the training set with pseudo-labeled examples. The extended dataset is used to train the final solution. Our method was ranked first in five out of ten language subtasks, obtaining the highest average score across all languages.

翻译：本文描述了我们在SemEval 2023多语言推文亲密性分析共享任务中的提交方案。该任务的目标是评估十个语言推特帖子的亲密程度。所提出的方法包含若干步骤：首先，我们进行域内预训练，构建适应推特数据的语言模型。其次，我们训练一个回归模型集成，利用伪标签样本扩展训练集。扩展后的数据集用于训练最终解决方案。我们的方法在十个语言子任务中夺得五项第一，并取得了所有语言上的最高平均得分。