iREL at SemEval-2023 Task 9: Improving understanding of multilingual Tweets using Translation-Based Augmentation and Domain Adapted Pre-Trained Models

Abstract

This paper describes our system (iREL) for Tweet intimacy analysis shared task of the Se-mEval 2023 workshop at ACL 2023. Our system achieved an overall Pearson’s r score of 0.5924 and ranked 10th on the overall leaderboard. For the unseen languages, we ranked third on the leaderboard and achieved a Pearson’s r score of 0.485. We used a single multilingual model for all languages, as discussed in this paper. We provide a detailed description of our pipeline along with multiple ablation experiments to further analyse each component of the pipeline. We demonstrate how translation-based augmentation, domain-specific features, and domain-adapted pretrained models improve the understanding of intimacy in tweets. The code can be found at https://github.com/bhavyajeet/Multilingual-tweet-intimacy.