2022/06/14 by Ahmed Mahran, Mahran, Ahmed, Carlo Alessandro Borella +3
Computer Science · Psychology · Social Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Humor Studies and Applications #Machine Learning (cs.LG) #Misinformation and Its Impacts
paper · pdf · doi:10.48550/arxiv.2206.07190
openalex publication_date 2022/06/14 · openalex created_date 2022/06/18 · openalex updated_date 2026/07/28
In this paper we describe our work towards building a generic framework for both multi-modal embedding and multi-label binary classification tasks, while participating in task 5 (Multimedia Automatic Misogyny Identification) of SemEval 2022 competition. Since pretraining deep models from scratch is a resource and data hungry task, our approach is based on three main strategies. We combine different state-of-the-art architectures to capture a wide spectrum of semantic signals from the multi-modal input. We employ a multi-task learning scheme to be able to use multiple datasets from the same knowledge domain to help increase the model's performance. We also use multiple objectives to regularize and fine tune different system components.