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Article type: Research Article
Authors: Liu, Jinga | Tian, Shengweia; * | Yu, Longb | Long, Junc; d | zhou, Tiejune | Wang, Boa
Affiliations: [a] School of Software, Xinjiang University, Xinjiang, China | [b] Network and Information Center, Xinjiang University, Xinjiang, China | [c] School of Information Science and Engineering, Central South University, Changsha, China | [d] Big Data and Knowledge Engineering Institute, Central South University, Changsha, China | [e] Xinjiang Internet Information Center, Xinjiang, China
Correspondence: [*] Corresponding author. Shengwei Tian, School of Software, Xinjiang University, Xinjiang, China. E-mail: tianshengwei@163.com.
Abstract: Sarcasm is a way to express the thoughts of a person. The intended meaning of the ideas expressed through sarcasm is often the opposite of the apparent meaning. Previous work on sarcasm detection mainly focused on the text. But nowadays most information is multi-modal, including text and images. Therefore, the task of targeting multi-modal sarcasm detection is becoming an increasingly hot research topic. In order to better detect the accurate meaning of multi-modal sarcasm information, this paper proposed a multi-modal fusion sarcasm detection model based on the attention mechanism, which introduced Vision Transformer (ViT) to extract image features and designed a Double-Layer Bi-Directional Gated Recurrent Unit (D-BiGRU) to extract text features. The features of the two modalities are fused into one feature vector and predicted after attention enhancement. The model presented in this paper gained significant experimental results on the baseline datasets, which are 0.71% and 0.38% higher than that of the best baseline model proposed on F1-score and accuracy respectively.
Keywords: Multi-modal, sarcasm detection, Attention, ViT, D-BiGRU
DOI: 10.3233/JIFS-213501
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2097-2108, 2023
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