Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection

Lambert Mathias, Shaoliang Nie, Aida Mostafazadeh Davani, Douwe Kiela, Vinodkumar Prabhakaran, Bertie Vidgen, Zeerak Waseem


Abstract
We present the results and main findings of the shared task at WOAH 5 on hateful memes detection. The task include two subtasks relating to distinct challenges in the fine-grained detection of hateful memes: (1) the protected category attacked by the meme and (2) the attack type. 3 teams submitted system description papers. This shared task builds on the hateful memes detection task created by Facebook AI Research in 2020.
Anthology ID:
2021.woah-1.21
Volume:
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | WOAH
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
201–206
Language:
URL:
https://aclanthology.org/2021.woah-1.21
DOI:
10.18653/v1/2021.woah-1.21
Bibkey:
Cite (ACL):
Lambert Mathias, Shaoliang Nie, Aida Mostafazadeh Davani, Douwe Kiela, Vinodkumar Prabhakaran, Bertie Vidgen, and Zeerak Waseem. 2021. Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection. In Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021), pages 201–206, Online. Association for Computational Linguistics.
Cite (Informal):
Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection (Mathias et al., WOAH 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.woah-1.21.pdf
Data
Hateful MemesHateful Memes Challenge