@inproceedings{arango-etal-2022-hateu,
    title = "{H}ate{U} at {S}em{E}val-2022 Task 5: Multimedia Automatic Misogyny Identification",
    author = "Arango, Ayme  and
      Perez-Martin, Jesus  and
      Labrada, Arniel",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.semeval-1.80/",
    doi = "10.18653/v1/2022.semeval-1.80",
    pages = "581--584",
    abstract = "Hate speech expressions in social media are not limited to textual messages; they can appear in videos, images, or multimodal formats like memes. Existing work towards detecting such expressions has been conducted almost exclusively over textual content, and the analysis of pictures and videos has been very scarce. This paper describes our team proposal in the Multimedia Automatic Misogyny Identification (MAMI) task at SemEval 2022. The challenge consisted of identifying misogynous memes from a dataset where images and text transcriptions were provided. We reported a 71{\%} of F-score using a multimodal system based on the CLIP model."
}