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多语言推特亲密度分析共享任务中提交的方案。该任务的目标是评估十种语言的推特帖子的亲密程度。所提出的方法包括几个步骤。首先，我们进行域内预训练，以创建适用于推特数据的语言模型。接下来，我们训练回归模型的集合，以伪标记的示例扩展训练集。扩展的数据集用于训练最终的解决方案。我们的方法在五个语言子任务中排名第一，在所有语言中获得最高平均分。
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