@inproceedings{rao-rao-2022-asrtrans,
    title = "{ASR}trans at {S}em{E}val-2022 Task 5: Transformer-based Models for Meme Classification",
    author = "Rao, Ailneni Rakshitha  and
      Rao, Arjun",
    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/ingest-emnlp/2022.semeval-1.82/",
    doi = "10.18653/v1/2022.semeval-1.82",
    pages = "597--604",
    abstract = "Women are frequently targeted online with hate speech and misogyny using tweets, memes, and other forms of communication. This paper describes our system for Task 5 of SemEval-2022: Multimedia Automatic Misogyny Identification (MAMI). We participated in both the sub-tasks, where we used transformer-based architecture to combine features of images and text. We explore models with multi-modal pre-training (VisualBERT) and text-based pre-training (MMBT) while drawing comparative results. We also show how additional training with task-related external data can improve the model performance. We achieved sizable improvements over baseline models and the official evaluation ranked our system $3^{rd}$ out of 83 teams on the binary classification task (Sub-task A) with an F1 score of 0.761, and $7^{th}$ out of 48 teams on the multi-label classification task (Sub-task B) with an F1 score of 0.705."
}