MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System
Libo Qin Shijue Huang Qiguang Chen Chenran Cai Yudi Zhang Bin Liang Wanxiang Che Ruifeng Xu
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.