论文标题
使用图形网络的早期融合模型使用厌恶女性模因检测
Misogynistic Meme Detection using Early Fusion Model with Graph Network
论文作者
论文摘要
近年来，以一种新的娱乐媒体形式激增了称为模因的新形式。尽管看似无害的模因超越了针对妇女的在线骚扰边界，并对她们产生了不必要的偏见。为了帮助缓解这个问题，我们提出了一个早期的融合模型，用于预测和识别厌恶女性模因及其类型的本文中，我们参与了Semeval-2022任务5。该模型作为输入模因图像及其文本转录带有目标向量。鉴于此任务的关键挑战是预测厌女症的不同方式的组合，我们的模型依赖于来自不同最先进的基于变压器的语言模型和预验证的图像预处理模型的上下文表示，以获得有效的图像表示。我们的模型与其他竞争团队在子任务-A和子任务-B上都取得了竞争成果，并显着优于基线。
In recent years , there has been an upsurge in a new form of entertainment medium called memes. These memes although seemingly innocuous have transcended onto the boundary of online harassment against women and created an unwanted bias against them . To help alleviate this problem , we propose an early fusion model for prediction and identification of misogynistic memes and its type in this paper for which we participated in SemEval-2022 Task 5 . The model receives as input meme image with its text transcription with a target vector. Given that a key challenge with this task is the combination of different modalities to predict misogyny, our model relies on pretrained contextual representations from different state-of-the-art transformer-based language models and pretrained image pretrained models to get an effective image representation. Our model achieved competitive results on both SubTask-A and SubTask-B with the other competition teams and significantly outperforms the baselines.
