@inproceedings{muti-etal-2022-unibo,
    title = "{U}ni{BO} at {S}em{E}val-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes",
    author = "Muti, Arianna  and
      Korre, Katerina  and
      Barr{\'o}n-Cede{\~n}o, Alberto",
    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/jlcl-multiple-ingestion/2022.semeval-1.91/",
    doi = "10.18653/v1/2022.semeval-1.91",
    pages = "663--672",
    abstract = "We present our submission to SemEval 2022 Task 5 on Multimedia Automatic Misogyny Identification. We address the two tasks: Task A consists of identifying whether a meme is misogynous. If so, Task B attempts to identify its kind among shaming, stereotyping, objectification, and violence. Our approach combines a BERT Transformer with CLIP for the textual and visual representations. Both textual and visual encoders are fused in an early-fusion fashion through a Multimodal Bidirectional Transformer with unimodally pretrained components. Our official submissions obtain macro-averaged F$_1$=0.727 in Task A (4th position out of 69 participants)and weighted F$_1$=0.710 in Task B (4th position out of 42 participants)."
}