论文标题
Hate-Alert@dravidianlangtech-ACL2022：泰米尔巨魔分类的多模式结合
hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for Tamil TrollMeme Classification
论文作者
论文摘要
社交媒体平台通常是针对用户或社区的各种形式的拖钓或恶意内容的繁殖场。拖钓用户的一种方法是创建模因，在大多数情况下，该模因将图像结合在一起，上面嵌入了一小段文本。由于缺乏基准数据集和模型，这种情况对于多语言（例如，泰米尔语）模因更为复杂。我们根据共享任务“ Dravidianlangtech2022中的巨魔模因分类”在ACL-2022上探索了几种模型来检测泰米尔语中的巨魔模因。我们观察到基于文本的模型Muril在非很多模因分类方面的表现更好，但基于图像的模型VGG16对巨魔效果分类的性能更好。进一步融合这两种方式有助于我们在两个课程中取得稳定的成果。我们的融合模型达到了0.561的加权平均F1得分，在此任务中排名第二。
Social media platforms often act as breeding grounds for various forms of trolling or malicious content targeting users or communities. One way of trolling users is by creating memes, which in most cases unites an image with a short piece of text embedded on top of it. The situation is more complex for multilingual(e.g., Tamil) memes due to the lack of benchmark datasets and models. We explore several models to detect Troll memes in Tamil based on the shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022. We observe while the text-based model MURIL performs better for Non-troll meme classification, the image-based model VGG16 performs better for Troll-meme classification. Further fusing these two modalities help us achieve stable outcomes in both classes. Our fusion model achieved a 0.561 weighted average F1 score and ranked second in this task.
