论文标题
semeval-2022任务5：通过图像情感和图形卷积网络增强单程卷积网络，以进行多媒体自动厌女症识别
UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification
论文作者
论文摘要
近来，由于社交媒体的指数增长和此类信息的传播及其影响，在线媒体中对仇恨语音，令人反感或滥用语言的检测已成为NLP研究中的重要话题。尽管厌女症的检测在仇恨语音检测中起着重要的作用，但也没有受到同样的关注。在本文中，我们描述了提交给Semeval -2022任务的分类系统5：MAMI-多媒体自动厌女症识别。共享任务旨在通过分析模因图像以及其文本字幕来识别多模式设置中的厌恶内容。为此，我们提出了两个基于预训练的Uniter模型的模型，一个模型通过图像情感分类器增强，而第二个模型则利用了词汇图卷积网络（VGCN）。此外，我们使用上述模型探索合奏。我们的最佳模型在子任务A中达到71.4％，子任务B为67.3％，将我们的团队定位在排行榜上方的三分之一。我们在GitHub上发布了模型的代码和实验
In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact. Misogyny detection, even though it plays an important part in hate-speech detection, has not received the same attention. In this paper, we describe our classification systems submitted to the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. The shared task aimed to identify misogynous content in a multi-modal setting by analysing meme images together with their textual captions. To this end, we propose two models based on the pre-trained UNITER model, one enhanced with an image sentiment classifier, whereas the second leverages a Vocabulary Graph Convolutional Network (VGCN). Additionally, we explore an ensemble using the aforementioned models. Our best model reaches an F1-score of 71.4% in Sub-task A and 67.3% for Sub-task B positioning our team in the upper third of the leaderboard. We release the code and experiments for our models on GitHub
