By: Douwe Kiela, Hamed Firooz and Tony Nelli Originally published in Facebook AI, Dec 11, 2020.
AI has made progress in detecting hate speech, but important and difficult technical challenges remain. Back in May 2020, Facebook AI partnered with Getty Images and DrivenData to launch the Hateful Memes Challenge, a first-of-its-kind $100K competition and data set to accelerate research on the problem of detecting hate speech that combines images and text. As part of the challenge, Facebook AI created a unique data set of 10,000+ new multimodal examples, using licensed images from Getty Images so that researchers could easily use them in their work.
More than 3,300 participants from around the world entered the Hateful Memes Challenge, and we are now sharing details on the winning entries. The top-performing teams were:
Ron Zhu link to code
Niklas Muennighoff link to code
Team HateDetectron: Riza Velioglu and Jewgeni Rose link to code
Team Kingsterdam: Phillip Lippe, Nithin Holla, Shantanu Chandra, Santhosh Rajamanickam, Georgios Antoniou, Ekaterina Shutova and Helen Yannakoudakis link to code
Vlad Sandulescu link to code
You can see the full leaderboard here. As part of the NeurIPS 2020 competition track, the top five winners will discuss their solutions and we facilitated a Q&A with participants from around the world. Each of these five implementations has been made open source and is available now.
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Hateful Memes Challenge Winners Machine Learning Times - The Predictive Analytics Times
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