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Indirect speech such as sarcasm achieves a constellation of discourse goalsin human communication. While the indirectness of figurative language warrantsspeakers to achieve certain pragmatic goals, it is challenging for AI agents tocomprehend such idiosyncrasies of human communication. Though sarcasmidentification has been a well-explored topic in dialogue analysis, forconversational systems to truly grasp a conversation's innate meaning andgenerate appropriate responses, simply detecting sarcasm is not enough; it isvital to explain its underlying sarcastic connotation to capture its trueessence. In this work, we study the discourse structure of sarcasticconversations and propose a novel task - Sarcasm Explanation in Dialogue (SED).Set in a multimodal and code-mixed setting, the task aims to generate naturallanguage explanations of satirical conversations. To this end, we curate WITS,a new dataset to support our task. We propose MAF (Modality Aware Fusion), amultimodal context-aware attention and global information fusion module tocapture multimodality and use it to benchmark WITS. The proposed attentionmodule surpasses the traditional multimodal fusion baselines and reports thebest performance on almost all metrics. Lastly, we carry out detailed analysesboth quantitatively and qualitatively.

When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues