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    <abstract>We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our 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. While prior works on sarcasm generation predominantly focus on context incongruity, we show that combining valence reversal and semantic incongruity based on the commonsense knowledge generates sarcasm of higher quality. Human evaluation shows that our system generates sarcasm better than humans 34% of the time, and better than a reinforced hybrid baseline 90% of the time.</abstract>
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%0 Conference Proceedings
%T R\³: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge
%A Chakrabarty, Tuhin
%A Ghosh, Debanjan
%A Muresan, Smaranda
%A Peng, Nanyun
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F chakrabarty-etal-2020-r
%X We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our 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. While prior works on sarcasm generation predominantly focus on context incongruity, we show that combining valence reversal and semantic incongruity based on the commonsense knowledge generates sarcasm of higher quality. Human evaluation shows that our system generates sarcasm better than humans 34% of the time, and better than a reinforced hybrid baseline 90% of the time.
%R 10.18653/v1/2020.acl-main.711
%U https://aclanthology.org/2020.acl-main.711
%U https://doi.org/10.18653/v1/2020.acl-main.711
%P 7976-7986

Markdown (Informal)

[Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge](https://aclanthology.org/2020.acl-main.711) (Chakrabarty et al., ACL 2020)

• Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge (Chakrabarty et al., ACL 2020)

ACL

• Tuhin Chakrabarty, Debanjan Ghosh, Smaranda Muresan, and Nanyun Peng. 2020. Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7976–7986, Online. Association for Computational Linguistics.