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SemIRNet: A Semantic Irony Recognition Network for Multimodal Sarcasm Detection

SemIRNet: A Semantic Irony Recognition Network for Multimodal Sarcasm Detection

来源:Arxiv_logoArxiv
英文摘要

Aiming at the problem of difficulty in accurately identifying graphical implicit correlations in multimodal irony detection tasks, this paper proposes a Semantic Irony Recognition Network (SemIRNet). The model contains three main innovations: (1) The ConceptNet knowledge base is introduced for the first time to acquire conceptual knowledge, which enhances the model's common-sense reasoning ability; (2) Two cross-modal semantic similarity detection modules at the word level and sample level are designed to model graphic-textual correlations at different granularities; and (3) A contrastive learning loss function is introduced to optimize the spatial distribution of the sample features, which improves the separability of positive and negative samples. Experiments on a publicly available multimodal irony detection benchmark dataset show that the accuracy and F1 value of this model are improved by 1.64% and 2.88% to 88.87% and 86.33%, respectively, compared with the existing optimal methods. Further ablation experiments verify the important role of knowledge fusion and semantic similarity detection in improving the model performance.

Jingxuan Zhou、Yuehao Wu、Yibo Zhang、Yeyubei Zhang、Yunchong Liu、Bolin Huang、Chunhong Yuan

计算技术、计算机技术

Jingxuan Zhou,Yuehao Wu,Yibo Zhang,Yeyubei Zhang,Yunchong Liu,Bolin Huang,Chunhong Yuan.SemIRNet: A Semantic Irony Recognition Network for Multimodal Sarcasm Detection[EB/OL].(2025-05-28)[2025-07-23].https://arxiv.org/abs/2506.14791.点此复制

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