Abstract
Hate speech expressions in social media are not limited to textual messages; they can appear in videos, images, or multimodal formats like memes. Existing work towards detecting such expressions has been conducted almost exclusively over textual content, and the analysis of pictures and videos has been very scarce. This paper describes our team proposal in the Multimedia Automatic Misogyny Identification (MAMI) task at SemEval 2022. The challenge consisted of identifying misogynous memes from a dataset where images and text transcriptions were provided. We reported a 71% of F-score using a multimodal system based on the CLIP model.
Cite
CITATION STYLE
Arango, A., Perez-Martin, J., & Labrada, A. (2022). HateU at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification. In SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop (pp. 581–584). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.semeval-1.80
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