{"url":"/dataset/mmsd2-0","name":"MMSD2.0","full_name":"Towards a Reliable Multi-modal Sarcasm Detection System","description_markdown":"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 (with a 5.6% improvement).","description_withheld":null,"homepage":"https://github.com/JoeYing1019/MMSD2.0","introduced_date":"2023-07-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/mmsd2-0-towards-a-reliable-multi-modal","title":"MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System","first_author":"Libo Qin","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Sarcasm Detection","url":"/task/sarcasm-detection","datasets_with_task":"/datasets/task/sarcasm-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MMSD2.0"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}