论文标题
semeval-2022任务5：多模式的多模式多转变器厌恶模因分类框架
Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework
论文作者
论文摘要
在本文中，我们描述了为多模式嵌入和多标签二进制分类任务构建通用框架的工作，同时参与了Semeval 2022竞争的任务5（多媒体自动厌食症）。 由于从头开始预处理模型是一项资源和数据饥饿的任务，因此我们的方法基于三种主要策略。我们结合了不同的最先进的体系结构，以从多模式输入中捕获广泛的语义信号。我们采用多任务学习方案，能够使用来自相同知识领域的多个数据集来帮助提高模型的性能。我们还使用多个目标来正规化和微调不同的系统组件。
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.
