I Didn'T Mean What I Wrote! Exploring Multimodality For Sarcasm Detection

Sarcasm detection is, inherently, a non-trivial problem where people express negative sentiment using positive insinuation words. Traditional approaches, in general, rely on the textual information to detect the incongruity between the surface meaning and the actual meaning. However, textual information is not always sufficient, and, often, other sources of information (e.g., visual) provides an important clue for sarcasm detection. In this paper, we propose an effective method based on deep learning that utilizes both textual and visual information for multi-modal sarcasm detection. Our proposed approach is based on the recurrent neural network that aims to exploit the interaction among the input modalities for the prediction. Experimental results suggest that the incorporation of visual modalities plays a decisive role in performance improvement.