论文标题
tib-va在Semeval-2022任务5：用于检测和分类厌恶模因的多模式结构
TIB-VA at SemEval-2022 Task 5: A Multimodal Architecture for the Detection and Classification of Misogynous Memes
论文作者
论文摘要
在社交媒体上发现进攻性，可恶的内容是一个充满挑战的问题，每天都会影响许多在线用户。仇恨的内容通常用于针对基于种族，性别，宗教和其他因素的一群人。对妇女的仇恨或蔑视在社交平台上一直在增加。当将文本和视觉方式组合在一起以形成单个上下文时，例如，嵌入在图像之上的覆盖文本（也称为Meme）时，厌恶的内容检测尤其具有挑战性。在本文中，我们提出了一种多模式体系结构，该体系结构结合了文本和视觉特征，以检测厌恶的模因内容。在Semeval-2022任务5：MAMI-Multimedia自动厌女症识别挑战中评估了所提出的体系结构。我们的解决方案在任务-B中获得了最佳结果，在该任务B中，挑战是分类给定文档是否是厌恶的，并进一步确定羞辱，刻板印象，客观化和暴力的主要子类。
The detection of offensive, hateful content on social media is a challenging problem that affects many online users on a daily basis. Hateful content is often used to target a group of people based on ethnicity, gender, religion and other factors. The hate or contempt toward women has been increasing on social platforms. Misogynous content detection is especially challenging when textual and visual modalities are combined to form a single context, e.g., an overlay text embedded on top of an image, also known as meme. In this paper, we present a multimodal architecture that combines textual and visual features in order to detect misogynous meme content. The proposed architecture is evaluated in the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification challenge under the team name TIB-VA. Our solution obtained the best result in the Task-B where the challenge is to classify whether a given document is misogynous and further identify the main sub-classes of shaming, stereotype, objectification, and violence.
