论文标题
使用文本和视觉提示的讽刺检测的多模式方法
A Multi-Modal Method for Satire Detection using Textual and Visual Cues
论文作者
论文摘要
讽刺是一种幽默的批评形式，但有时会被读者误解为合法的新闻，这可能导致有害后果。我们观察到讽刺新闻文章中使用的图像通常包含荒谬或荒谬的内容，并且该图像操纵用于创建虚构的场景。尽管以前的工作研究了基于文本的方法，但在这项工作中，我们提出了一种基于最先进的Visiol语言模型Vilbert的多模式方法。为此，我们创建了一个新的数据集，该数据集由图像和常规和讽刺新闻的头条组成，以实现讽刺探测任务。我们在数据集中微调Vilbert，并训练使用图像取证技术的卷积神经网络。数据集上的评估表明，我们提出的多模式方法的表现优于图像，仅文本和简单的融合基线。
Satire is a form of humorous critique, but it is sometimes misinterpreted by readers as legitimate news, which can lead to harmful consequences. We observe that the images used in satirical news articles often contain absurd or ridiculous content and that image manipulation is used to create fictional scenarios. While previous work have studied text-based methods, in this work we propose a multi-modal approach based on state-of-the-art visiolinguistic model ViLBERT. To this end, we create a new dataset consisting of images and headlines of regular and satirical news for the task of satire detection. We fine-tune ViLBERT on the dataset and train a convolutional neural network that uses an image forensics technique. Evaluation on the dataset shows that our proposed multi-modal approach outperforms image-only, text-only, and simple fusion baselines.
