VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes (bibtex)
by Piush Aggarwal, Michelle Espranita Liman, Darina Gold and Torsten Zesch
Abstract:
This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021. We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags such as race, gender, and web entities. We add further metadata such as emotions and experiment with data augmentation techniques, as hateful instances are underrepresented in the data set.
Reference:
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes Piush Aggarwal, Michelle Espranita Liman, Darina Gold and Torsten Zesch, In Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021), Association for Computational Linguistics, 2021.
Bibtex Entry:
@inproceedings{aggarwal-etal-2021-vl,
    title = {{VL}-{BERT}+: Detecting Protected Groups in Hateful Multimodal Memes},
    author = {Aggarwal, Piush  and Liman, Michelle Espranita  and Gold, Darina  and Zesch, Torsten},
    booktitle = "Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.woah-1.22",
    doi = "10.18653/v1/2021.woah-1.22",
    pages = "207--214",
    abstract = "This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021. We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags such as race, gender, and web entities. We add further metadata such as emotions and experiment with data augmentation techniques, as hateful instances are underrepresented in the data set.",
}