While prior works on sarcasm generation predominantly focus on context incongruity, this research shows that combining valence reversal and semantic incongruity based on commonsense knowledge generates sarcastic messages of higher quality based on several criteria. This method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context, which could include shared commonsense or world knowledge between the speaker and the listener. The paper proposes an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence.

R3 : Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge Tuhin Chakrabarty Columbia University, Debanjan Ghosh Educational Testing Service, Smaranda Muresan Columbia University, and Nanyun Peng University of Southern California Kathleen McKeown, the Henry and Gertrude Rothschild Professor of Computer Science, also gave a keynote presentation вЂ“ вЂњRewriting the Past: Assessing the Field through the Lens of Language GenerationвЂќ. The research developed systems that improve fact-checking, generate sarcasm, and detect deception and a neural network that extracts knowledge about products on Amazon.ДЄ team led by Shih-Fu Chang, the Richard Dicker Professor and Senior Executive Vice Dean School of Engineering, won a Best Demonstration Paper award for GAIA: A Fine-grained Multimedia Knowledge Extraction System, the first comprehensive, open-source multimedia knowledge extraction system. Papers from the Speech and Natural Language Processing groups were accepted to the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020).

Acl 2020