Multi-Modal Sarcasm Detection Based on Contrastive Attention Mechanism

• URL: http://arxiv.org/abs/2109.15153v1
• Date: Thu, 30 Sep 2021 14:17:51 GMT
• Title: Multi-Modal Sarcasm Detection Based on Contrastive Attention Mechanism
• Authors: Xiaoqiang Zhang, Ying Chen, Guangyuan Li
• Abstract summary: We construct a Contras-tive-Attention-based Sarcasm Detection (ConAttSD) model, which uses an inter-modality contrastive attention mechanism to extract contrastive features for an utterance. Our experiments on MUStARD, a benchmark multi-modal sarcasm dataset, demonstrate the effectiveness of the proposed ConAttSD model.
• Score: 7.194040730138362
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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