@inproceedings{montariol-etal-2022-fine,
    title = "Fine-tuning and Sampling Strategies for Multimodal Role Labeling of Entities under Class Imbalance",
    author = "Montariol, Syrielle  and
      Simon, {\'E}tienne  and
      Riabi, Arij  and
      Seddah, Djam{\'e}",
    editor = "Chakraborty, Tanmoy  and
      Akhtar, Md. Shad  and
      Shu, Kai  and
      Bernard, H. Russell  and
      Liakata, Maria  and
      Nakov, Preslav  and
      Srivastava, Aseem",
    booktitle = "Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2022.constraint-1.7/",
    doi = "10.18653/v1/2022.constraint-1.7",
    pages = "55--65",
    abstract = "We propose our solution to the multimodal semantic role labeling task from the CONSTRAINT{'}22 workshop. The task aims at classifying entities in memes into classes such as ``hero'' and ``villain''. We use several pre-trained multi-modal models to jointly encode the text and image of the memes, and implement three systems to classify the role of the entities. We propose dynamic sampling strategies to tackle the issue of class imbalance. Finally, we perform qualitative analysis on the representations of the entities."
}