Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework
Ahmed Mahran, Carlo Alessandro Borella, Konstantinos Perifanos · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
In this paper we describe our work towards building a generic framework for both multimodal 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 multitask 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.