论文标题
仇恨模因挑战：在多模式模因中检测仇恨言论
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
论文作者
论文摘要
这项工作提出了针对多模式分类的新挑战集，重点是检测多模式模因中的仇恨言论。它的构造使单峰模型斗争，只有多模型模型才能成功：艰难的示例（“良性混杂器”）被添加到数据集中，以使很难依靠单峰信号。该任务需要微妙的推理，但要简单地评估为二进制分类问题。我们为单峰模型以及具有不同程度复杂程度的多模型模型提供基线性能编号。我们发现，与人类相比，最新方法的性能较差（64.73％vs. 84.7％的准确性），这说明了任务的困难，并强调了这个重要问题对社区带来的挑战。
This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. It is constructed such that unimodal models struggle and only multimodal models can succeed: difficult examples ("benign confounders") are added to the dataset to make it hard to rely on unimodal signals. The task requires subtle reasoning, yet is straightforward to evaluate as a binary classification problem. We provide baseline performance numbers for unimodal models, as well as for multimodal models with various degrees of sophistication. We find that state-of-the-art methods perform poorly compared to humans (64.73% vs. 84.7% accuracy), illustrating the difficulty of the task and highlighting the challenge that this important problem poses to the community.
