  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.
