While the unimodal-text context-aware (transformer) model was the most accurate on the subtask of implicit hate detection, the multimodal model outperformed it overall because of a lower propensity towards false positives. We find that all models perform better on content with full annotator agreement and that multimodal models are best at classifying the content where annotators disagree. https://aclanthology.org/2021.findings-acl.166/ Share this: Print (Opens in new window) Print Share on Facebook (Opens in new window) Facebook Share on LinkedIn (Opens in new window) LinkedIn Share on Reddit (Opens in new window) Reddit Share on WhatsApp (Opens in new window) WhatsApp Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Like this:Like Loading... Post navigation Deep learning based fusion strategies for hate speech detection to combine the classifiers to improve classification performance (AIP Conference Procedingsw) Free Resources on Countering Extremism and Hate Speech, September 2023 (I/II)