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MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System

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arxiv 2307.07135 v1 pith:VCKB3ALA submitted 2023-07-14 cs.CL

Libo Qin , Shijue Huang , Qiguang Chen , Chenran Cai , Yudi Zhang , Bin Liang , Wanxiang Che , Ruifeng Xu This is my paper
classification cs.CL
keywords detectionmulti-modalsarcasmmmsdcuesmmsd2reliablebenchmark
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

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  1. Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detection

    cs.AI 2025-06 unverdicted novelty 5.0 of 10

    Commander-GPT is a multi-agent routing framework that assigns sub-tasks in multimodal sarcasm detection to specialized LLMs coordinated by different commander models, reporting average F1 gains of 4.4% and 11.7% on MM...

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