R ˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge (2020)

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.

## introduction studies have shown that the use of sarcasm or verbal irony, can increase creativity on both the speakers and the addressees @xcite , and can serve different communicative purposes such as evoking humor and diminishing or enhancing critique @xcite . thus, developing computational models that generate sarcastic messages could impact many downstream applications, such as better conversational agents and creative or humorous content creation. while most computational work has focused on sarcasm detection @xcite @xcite @xcite @xcite @xcite , research on sarcasm generation is in its infancy @xcite . sarcasm generation literal input 1 i hate getting sick from fast food. ## sarcasm factors used in generation a sarcastic utterance must satisfy the sarcasm factors, i.e., the inherent characteristics of sarcasm @xcite . in this research, we leverage the use of two particular factors to generate sarcasm. one is the reversal of valence and the other is the semantic incongruity with the context, which could include shared commonsense or world knowledge between the speaker and the hearer. ## unsupervised sarcasm generation an overview of the sarcasm generation pipeline is shown in figure 1 . in this section, we detail the three main modules that are designed to instantiate the key sarcasm factors. ## qualitative analysis we also observe that turkers consistently rated generations from the fm model more sarcastic than the nsi model suggesting that there is a correlation between human scores of sarcasticness and incongruity. to support this observation, we took the contradiction scores from the roberta model for both best ranked retrieved sentences (fm) and the randomly selected retrieved sentences (nsi). we then computed a correlation between the sarcasticness scores given by the humans and the automatic contradiction scores for both the best ranked retrieved sentences (fm) and the randomly selected retrieved sentences (nsi). for fm model we obtain a higher pearson correlation coefficient compared to nsi suggesting the important role of incongruency for sarcasm. ## conclusion we address the problem of unsupervised sarcasm generation that models several sarcasm factors including reversal of valence and semantic incongruity with the context. the key contribution of our approach is the modeling of commonsense knowledge in a retrieve-and-edit generation framework. a human-based evaluation based on four criteria shows that our generation approach significantly outperforms a state-of-the-art model. compared with human generated sarcasm, our model shows promise particularly for creativity, humor and sarcasticness, but less for grammaticality. a bigger challenge in sarcasm generation and more generally, creative text generation, is to capture the difference between creativity (novel but well-formed material) and nonsense (ill-formed material). language models conflate the two, so developing methods that are nuanced enough to recognize this difference is key to future progress.