@inproceedings{singh-etal-2023-irel,
    title = "i{REL} at {S}em{E}val-2023 Task 9: Improving understanding of multilingual Tweets using Translation-Based Augmentation and Domain Adapted Pre-Trained Models",
    author = "Singh, Bhavyajeet  and
      Maity, Ankita  and
      Kandru, Pavan  and
      Hari, Aditya  and
      Varma, Vasudeva",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.282",
    doi = "10.18653/v1/2023.semeval-1.282",
    pages = "2052--2057",
    abstract = "This paper describes our system (iREL) for Tweet intimacy analysis sharedtask of the SemEval 2023 workshop at ACL 2023. Oursystem 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 pre-trained models improve the understanding of intimacy in tweets. The codecan be found at {\textbackslash}href{https://github.com/bhavyajeet/Multilingual-tweet-intimacy}{https://github.com/bhavyajeet/Multilingual-tweet-intimacy}",
}

This paper describes our system (iREL) for Tweet intimacy analysis sharedtask of the SemEval 2023 workshop at ACL 2023. Oursystem 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 pre-trained models improve the understanding of intimacy in tweets. The codecan be found at {\textbackslash}href{https://github.com/bhavyajeet/Multilingual-tweet-intimacy}{https://github.com/bhavyajeet/Multilingual-tweet-intimacy}
Singh, Bhavyajeet Maity, Ankita K ru, Pavan Hari, Aditya Varma, Vasudeva
iREL at SemEval-2023 Task 9: Improving understanding of multilingual Tweets using Translation-Based Augmentation and Domain Adapted Pre-Trained Models