RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs
Wentao Yu, Benedikt Boenninghoff, Jonas Röhrig, Dorothea Kolossa · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification.The challenge organizers provide an English meme dataset to develop and train systems for identifying and classifying misogynous memes.More precisely, the competition is separated into two sub-tasks: sub-task A asks for a binary decision as to whether a meme expresses misogyny, while sub-task B is to classify misogynous memes into the potentially overlapping sub-categories of stereotype, shaming, objectification, and violence.For our submission, we implement a new model fusion network and employ an ensemble learning approach for better performance.With this structure, we achieve a 0.755 macroaverage F1-score (11th) in sub-task A and a 0.709 weighted-average F1-score (10th) in subtask B. 11 Code available at: https://github.com/ rub-ksv/SemEval-Task5-MAMI.