论文标题
AMS_ADRN在Semeval-2022任务5：一种合适的图像文本多模式建模方法，用于多任务厌女症识别
AMS_ADRN at SemEval-2022 Task 5: A Suitable Image-text Multimodal Joint Modeling Method for Multi-task Misogyny Identification
论文作者
论文摘要
女性在网上具有影响力，尤其是在基于图像的社交媒体中，例如Twitter和Instagram。但是，网络环境中的许多人都包含性别歧视和积极的信息，这会放大性别刻板印象和性别不平等。因此，过滤诸如性别歧视之类的非法内容对于维持健康的社交网络环境至关重要。在本文中，我们描述了我们团队为Semeval-2022任务开发的系统5：多媒体自动厌女症识别。更具体地说，我们介绍了两个新型系统来分析以下帖子：多模式的多任务学习体系结构，将Bertweet结合在一起，将文本编码与RESNET-18的文本编码结合在一起，用于图像表示形式，以及一个单流变压器结构，结合了来自Bert-Embeddings和Force Nicthand Modules的文本嵌入式的单流动器结构，例如来自几个不同的模块。通过这种方式，我们表明可以正确揭示其背后的信息。我们的方法在当前竞赛的两个子任务中的每个子任务中都取得了良好的表现，在子任务A（0.746宏观F1得分）中排名第15位，子任务B（0.706宏观F1得分）排名第11，而超过官方的基线结果。
Women are influential online, especially in image-based social media such as Twitter and Instagram. However, many in the network environment contain gender discrimination and aggressive information, which magnify gender stereotypes and gender inequality. Therefore, the filtering of illegal content such as gender discrimination is essential to maintain a healthy social network environment. In this paper, we describe the system developed by our team for SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification. More specifically, we introduce two novel system to analyze these posts: a multimodal multi-task learning architecture that combines Bertweet for text encoding with ResNet-18 for image representation, and a single-flow transformer structure which combines text embeddings from BERT-Embeddings and image embeddings from several different modules such as EfficientNet and ResNet. In this manner, we show that the information behind them can be properly revealed. Our approach achieves good performance on each of the two subtasks of the current competition, ranking 15th for Subtask A (0.746 macro F1-score), 11th for Subtask B (0.706 macro F1-score) while exceeding the official baseline results by high margins.
