Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework
This work addresses the challenge of identifying misogyny in memes for social media monitoring, but it is incremental as it builds on existing methods without introducing a new paradigm.
The paper tackles the problem of multi-modal misogynous meme classification by developing a framework that combines state-of-the-art architectures, multi-task learning, and multiple objectives, achieving competitive results in the SemEval 2022 competition.
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.