RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs
This work addresses the challenge of detecting misogyny in online memes, which is an incremental improvement in a specific domain.
The paper tackled the problem of identifying and classifying misogynous memes using an ensemble system, achieving a 0.755 macroaverage F1-score in binary classification and a 0.709 weighted-average F1-score in multi-label classification.
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 sub-task B.