Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge

Abstract

We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-andedit 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 commonsense knowledge generates sarcastic messages of higher quality based on several criteria. Human evaluation shows that our system generates sarcasm better than human judges 34% of the time, and better than a reinforced hybrid baseline 90% of the time.

Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions

“…Finally, few recent works focused on augmenting grounding with commonsense knowledge with successful applications in open-domain topical dialog generation (Ghazvininejad et al, 2018;Moon et al, 2019), story generation (Mao et al, 2019) and sarcasm generation (Chakrabarty et al, 2020). In this work, we extend this effort into persona-grounded dialog generation via augmenting grounding persona with commonsense knowledge.…”

Chandler: An Explainable Sarcastic Response Generator

“…Finally, the Sarcasm Synthesis module constructs the sarcastic paraphrase from u (+) and v (−) . Chakrabarty et al (2020) present a similar pipeline. Their R 3 system first employs a Reversal of Valence module, which replaces input words of negative valence with their lexical antonyms using WordNet (Miller, 1995) to produce u (+) .…”

“…These would be more effective at emulating a human correspondent, considering that sarcasm is a natural part of human discourse (Mishra et al, 2019). The limited amount of work on sarcasm generation is spread across two variants of the task: generating a sarcastic response to an input utterance (Joshi et al, 2015); and generating a sarcastic paraphrase of an input utterance (Mishra et al, 2019;Chakrabarty et al, 2020).…”

“…A major limitation of existing sarcasm generation systems is that they rely on variants of the traditional theory of sarcasm: that the intended meaning concealed by sarcasm is the opposite of the literal meaning. Driven by this assumption, their aim is to generate phrases that either express two incongruous propositions (Joshi et al, 2015;Mishra et al, 2019;Chakrabarty et al, 2020), or express a proposition that is incongruous to the discourse setting (Joshi et al, 2015). However, the traditional theory provides a grounding that is neither necessary, nor sufficient, for sarcasm to occur, as discussed in Section 3.…”

¡Qué maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline

“…In this section, we will present some of the recent related work on sarcasm detection. There has been some work also on sarcasm generation (Chakrabarty et al, 2020) and interpretation (Peled and Reichart, 2017), but they are rather different as tasks and we will not discuss them in detail. Badlani et al (2019) show an approach for sarcasm detection in online reivews.…”