CANs: Coupled-Attention Networks for Sarcasm Detection on Social Media

In recent years, sarcasm detection on social media has become one of the major challenges in natural language processing, due to the figurative characteristics of sarcasm. With the rise of social media platforms, especially Twitter, which allows users to post textual content while attaching a complement (such as image and video). A prevailing trend is to achieve a satire effect via combinations of text and image on Twitter. Therefore, to overcome the above-mentioned difficulty of sarcasm detection on social media, the key is to establish a multi-modal framework that can synchronically capture the information contained in text and vision. In this research, we propose Coupled-Attention Networks (CANs), which can effectively integrate information of text and image into a unified framework, thus realizing the fusion of different forms of resources. We conduct our experiments on a real-world dataset. Experimental results prove that our method achieves excellent results.