2021/09/30 by Xiaoqiang Zhang, Ying Chen, Zhang, Xiaoqiang +3 · 2 citations
Computer Science · Psychology · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Language, Metaphor, and Cognition #Sentiment Analysis and Opinion Mining #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2109.15153
NLPCC 2021
arxiv created 2021/09/30 · openalex publication_date 2021/09/30 · arxiv updated 2021/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the past decade, sarcasm detection has been intensively conducted in a textual scenario. With the popularization of video communication, the analysis in multi-modal scenarios has received much attention in recent years. Therefore, multi-modal sarcasm detection, which aims at detecting sarcasm in video conversations, becomes increasingly hot in both the natural language processing community and the multi-modal analysis community. In this paper, considering that sarcasm is often conveyed through incongruity between modalities (e.g., text expressing a compliment while acoustic tone indicating a grumble), we construct a Contras-tive-Attention-based Sarcasm Detection (ConAttSD) model, which uses an inter-modality contrastive attention mechanism to extract several contrastive features for an utterance. A contrastive feature represents the incongruity of information between two modalities. Our experiments on MUStARD, a benchmark multi-modal sarcasm dataset, demonstrate the effectiveness of the proposed ConAttSD model.