@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",
editor = "Mostafazadeh Davani, Aida and
Kiela, Douwe and
Lambert, Mathias and
Vidgen, Bertie and
Prabhakaran, Vinodkumar and
Waseem, Zeerak",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes
%A Aggarwal, Piush
%A Liman, Michelle Espranita
%A Gold, Darina
%A Zesch, Torsten
%Y Mostafazadeh Davani, Aida
%Y Kiela, Douwe
%Y Lambert, Mathias
%Y Vidgen, Bertie
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F aggarwal-etal-2021-vl
%X 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.
%R 10.18653/v1/2021.woah-1.22
%U https://aclanthology.org/2021.woah-1.22
%U https://doi.org/10.18653/v1/2021.woah-1.22
%P 207-214
Markdown (Informal)
[VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes](https://aclanthology.org/2021.woah-1.22) (Aggarwal et al., WOAH 2021)
ACL