Multi-modal sarcasm detection has attracted much recent attention. Nevertheless, the existing benchmark (MMSD) has some shortcomings that hinder the development of reliable multi-modal sarcasm detection system: (1) There are some spurious cues in MMSD, leading to the model bias learning; (2) The negative samples in MMSD are not always reasonable. To solve the aforementioned issues, we introduce MMSD2.0, a correction dataset that fixes the shortcomings of MMSD, by removing the spurious cues and re-annotating the unreasonable samples. Meanwhile, we present a novel framework called multi-view CLIP that is capable of leveraging multi-grained cues from multiple perspectives (i.e., text, image, and text-image interaction view) for multi-modal sarcasm detection. Extensive experiments show that MMSD2.0 is a valuable benchmark for building reliable multi-modal sarcasm detection systems and multi-view CLIP can significantly outperform the previous best baselines.

翻译：多模态讽刺检测近来备受关注。然而，现有基准（MMSD）存在一些缺陷，阻碍了可靠的多模态讽刺检测系统的发展：（1）MMSD中存在一些虚假线索，导致模型产生偏见性学习；（2）MMSD中的负样本并非总是合理的。为解决上述问题，我们提出了MMSD2.0，一个修正数据集，通过移除虚假线索并重新标注不合理样本来修复MMSD的缺陷。同时，我们提出了一种名为多视角CLIP的新框架，该框架能够从多个视角（即文本、图像以及文本-图像交互视角）利用多粒度线索进行多模态讽刺检测。大量实验表明，MMSD2.0是构建可靠多模态讽刺检测系统的宝贵基准，且多视角CLIP能显著超越先前最优基线模型。