@inproceedings{muti-barron-cedeno-2022-checkpoint,
    title = "A Checkpoint on Multilingual Misogyny Identification",
    author = "Muti, Arianna  and
      Barr{\'o}n-Cede{\~n}o, Alberto",
    editor = "Louvan, Samuel  and
      Madotto, Andrea  and
      Madureira, Brielen",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.acl-srw.37/",
    doi = "10.18653/v1/2022.acl-srw.37",
    pages = "454--460",
    abstract = "We address the problem of identifying misogyny in tweets in mono and multilingual settings in three languages: English, Italian, and Spanish. We explore model variations considering single and multiple languages both in the pre-training of the transformer and in the training of the downstream taskto explore the feasibility of detecting misogyny through a transfer learning approach across multiple languages. That is, we train monolingual transformers with monolingual data, and multilingual transformers with both monolingual and multilingual data. Our models reach state-of-the-art performance on all three languages. The single-language BERT models perform the best, closely followed by different configurations of multilingual BERT models. The performance drops in zero-shot classification across languages. Our error analysis shows that multilingual and monolingual models tend to make the same mistakes."
}