The paper describes a transformer-based system designed for SemEval-2023 Task
9: Multilingual Tweet Intimacy Analysis. The purpose of the task was to predict
the intimacy of tweets in a range from 1 (not intimate at all) to 5 (very
intimate). The official training set for the competition consisted of tweets in
six languages (English, Spanish, Italian, Portuguese, French, and Chinese). The
test set included the given six languages as well as external data with four
languages not presented in the training set (Hindi, Arabic, Dutch, and Korean).
We presented a solution based on an ensemble of XLM-T, a multilingual RoBERTa
model adapted to the Twitter domain. To improve the performance of unseen
languages, each tweet was supplemented by its English translation. We explored
the effectiveness of translated data for the languages seen in fine-tuning
compared to unseen languages and estimated strategies for using translated data
in transformer-based models. Our solution ranked 4th on the leaderboard while
achieving an overall Pearson's r of 0.599 over the test set. The proposed
system improves up to 0.088 Pearson's r over a score averaged across all 45
submissions.

本文基于 XLM-T 提出了一种基于 transformer 的系统，用于预测多语言推特的亲密度，使用英语翻译数据可用于优化训练和推理，取得了 0.599 的总体 Pearson's r 值和排名第四的成绩。