@inproceedings{kun-etal-2022-logically,
    title = "Logically at the Constraint 2022: Multimodal role labelling",
    author = "Kun, Ludovic  and
      Bankoti, Jayesh  and
      Kiskovski, David",
    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/ingest-emnlp/2022.constraint-1.4/",
    doi = "10.18653/v1/2022.constraint-1.4",
    pages = "24--34",
    abstract = "This paper describes our system for the Constraint 2022 challenge at ACL 2022, whose goal is to detect which entities are glorified, vilified or victimised, within a meme . The task should be done considering the perspective of the meme{'}s author. In our work, the challenge is treated as a multi-class classification task. For a given pair of a meme and an entity, we need to classify whether the entity is being referenced as Hero, a Villain, a Victim or Other. Our solution combines (ensembling) different models based on Unimodal (Text only) model and Multimodal model (Text + Images). We conduct several experiments and benchmarks different competitive pre-trained transformers and vision models in this work. Our solution, based on an ensembling method, is ranked first on the leaderboard and obtains a macro F1-score of 0.58 on test set. The code for the experiments and results are available at \url{https://bitbucket.org/logicallydevs/constraint_2022/src/master/}"
}