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.

该论文描述了他们在 SemEval 2022 竞赛的任务 5（多媒体自动仇恨辨别）中，构建通用框架以处理多模式嵌入和多标签二进制分类任务的工作。为了避免深度模型从零开始的资源和数据饥饿问题，作者采用三种主要策略，即组合不同的先进架构来捕捉来自多模态输入的广泛语义信号，采用多任务学习模式来利用来自同一领域的多个数据集以提高模型性能以及使用多个目标来规范和微调不同的系统组件。