@inproceedings{garcia-diaz-etal-2023-umuteam-sinai,
    title = "{UMUT}eam and {SINAI} at {S}em{E}val-2023 Task 9: Multilingual Tweet Intimacy Analysis using Multilingual Large Language Models and Data Augmentation",
    author = "Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio  and
      Pan, Ronghao  and
      Jim{\'e}nez Zafra, Salud Mar{\'\i}a  and
      Martn-Valdivia, Mar{\'\i}a-Teresa  and
      Ure{\~n}a-L{\'o}pez, L. Alfonso  and
      Valencia-Garc{\'\i}a, Rafael",
    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-1.39",
    doi = "10.18653/v1/2023.semeval-1.39",
    pages = "293--299",
    abstract = "This work presents the participation of the UMUTeam and the SINAI research groups in the SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis. The goal of this task is to predict the intimacy of a set of tweets in 10 languages: English, Spanish, Italian, Portuguese, French, Chinese, Hindi, Arabic, Dutch and Korean, of which, the last 4 are not in the training data. Our approach to address this task is based on data augmentation and the use of three multilingual Large Language Models (multilingual BERT, XLM and mDeBERTA) by ensemble learning. Our team ranked 30th out of 45 participants. Our best results were achieved with two unseen languages: Korean (16th) and Hindi (19th).",
}

This work presents the participation of the UMUTeam and the SINAI research groups in the SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis. The goal of this task is to predict the intimacy of a set of tweets in 10 languages: English, Spanish, Italian, Portuguese, French, Chinese, Hindi, Arabic, Dutch and Korean, of which, the last 4 are not in the training data. Our approach to address this task is based on data augmentation and the use of three multilingual Large Language Models (multilingual BERT, XLM and mDeBERTA) by ensemble learning. Our team ranked 30th out of 45 participants. Our best results were achieved with two unseen languages: Korean (16th) and Hindi (19th).
Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio Pan, Ronghao Jim{\'e}nez Zafra, Salud Mar{\'\i}a Martn-Valdivia, Mar{\'\i}a-Teresa Ure{\~n}a-L{\'o}pez, L. Alfonso Valencia-Garc{\'\i}a, Rafael
UMUTeam and SINAI at SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis using Multilingual Large Language Models and Data Augmentation