Multimodal Sarcasm Target Identification in Tweets
Jiquan Wang, Lin Sun, Yi Liu, Meizhi Shao, Zengwei Zheng
Abstract
Sarcasm is important to sentiment analysis on social media. Sarcasm Target Identification (STI) deserves further study to understand sarcasm in depth. However, text lacking context or missing sarcasm target makes target identification very difficult. In this paper, we introduce multimodality to STI and present Multimodal Sarcasm Target Identification (MSTI) task. We propose a novel multi-scale crossmodality model that can simultaneously perform textual target labeling and visual target detection. In the model, we extract multi-scale visual features to enrich spatial information for different sized visual sarcasm targets. We design a set of convolution networks to unify multi-scale visual features with textual features for cross-modal attention learning, and correspondingly a set of transposed convolution networks to restore multi-scale visual information. The results show that visual clues can improve the performance of TSTI by a large margin, and VSTI achieves good accuracy.