| Challenge: | Existing systems for sarcasm generation are elusive due to the fact that both selection of contents and training of sarcasm are based on the same data. |
| Approach: | They propose a framework that takes a literal negative opinion as input and translates it into a sarcastic version. |
| Outcome: | The proposed system outperforms baselines built using known unsupervised statistical and neural machine translation and style transfer techniques. |
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Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge (2020.acl-main)
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| Challenge: | Existing work on sarcasm generation focuses on context incongruity, but new work addresses this problem . |
| Approach: | They propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. |
| Outcome: | The proposed method generates sarcasm better than humans 34% of the time and better than a reinforced hybrid baseline 90% of the times. |
“When Words Fail, Emojis Prevail”: A Novel Architecture for Generating Sarcastic Sentences With Emoji Using Valence Reversal and Semantic Incongruity (2023.acl-srw)
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Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, Mohsinul Kabir, Hasan Mahmud, Md Kamrul Hasan
| Challenge: | Existing sarcasm generation tasks focus on textual sarcasm, but people often use emojis to express their emotions. |
| Approach: | They propose a novel architecture for sarcasm generation with emojis from a non-sarcastic input sentence in English. |
| Outcome: | The proposed architecture generates sarcastic outputs with emojis from a non-sarcastic input sentence in english. |
The Design and Construction of a Chinese Sarcasm Dataset (2020.lrec-1)
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| Challenge: | Existing sarcasm datasets are limited to English and Chinese . sarcasm is a multi-layered semi-conscious language phenomenon . |
| Approach: | They propose to build a high-quality Chinese sarcasm dataset using user comments . they use manual annotated sarkastic texts and non-sarcastic texts to train sarcasm classifier . |
| Outcome: | The proposed dataset contains 2,486 manual annotated sarcastic texts and 89,296 non-sarcatic texts. |
Multi-modal Sarcasm Generation: Dataset and Solution (2023.findings-acl)
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| Challenge: | Existing studies on sarcasm generation do not consider generating sarcasastic descriptions for a given image with hashtags that provide the sarkastic target. |
| Approach: | They propose a multi-modal Sarcasm generation task that generates sarcastic descriptions like humans using images, hashtags, and OCR tokens. |
| Outcome: | The proposed method can generate sarcastic descriptions like humans using 5000 images and Twitter text. |
A Large Self-Annotated Corpus for Sarcasm (L18-1)
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| Challenge: | Existing datasets for sarcasm detection have unbalanced and self-annotated labels, allowing for learning in both balanced and unbalanciated label regimes. |
| Approach: | They introduce the Self-Annotated Reddit Corpus (SARC) which has 1.3 million sarcastic statements and many times more instances of non-sarcasm statements. |
| Outcome: | The proposed corpus has 1.3 million sarcastic statements and many more instances of non-sarcasm statements, allowing for learning in both balanced and unbalanced label regimes. |
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning (2025.coling-main)
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| Challenge: | Existing methods for sarcasm detection lack commonsense inferential ability when faced with complex situations. |
| Approach: | They propose a commonsense reasoning framework for sarcasm detection based on commonsensense augmentation to supplement commonsence knowledge and infer the incongruity. |
| Outcome: | The proposed framework is able to detect sarcasm in five datasets and is robust to complex scenarios. |
Affective and Contextual Embedding for Sarcasm Detection (2020.coling-main)
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| Challenge: | Existing methods to detect sarcasm from text lack vocal intonation or facial gestures in textual data. |
| Approach: | They propose two deep neural network models for sarcasm detection that extend the architecture of BERT by incorporating both affective and contextual features. |
| Outcome: | The proposed models outperform state-of-the-art models on different datasets with significant margins. |
Chandler: An Explainable Sarcastic Response Generator (2021.emnlp-demo)
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| Challenge: | sarcasm generators assume intended meaning is opposite of literal meaning . sarcastically generated responses are more specific and coherent to input . |
| Approach: | They propose a system that generates sarcastic responses to a given utterance . they ground their generation process on a formal theory that unambiguously differentiates . |
| Outcome: | The proposed system generates sarcastic responses to a given utterance. |
Should a Chatbot be Sarcastic? Understanding User Preferences Towards Sarcasm Generation (2022.acl-long)
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| Challenge: | sarcasm generation research focused on creating more human-like interactions . previous research focused only on how to generate text that people perceive as sarkastic . |
| Approach: | They propose a theory-driven framework for generating sarcastic responses that allows us to control linguistic devices included during generation. |
| Outcome: | The proposed framework allows us to control the linguistic devices included during generation. |
RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection (2026.acl-long)
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| Challenge: | Existing approaches to sarcastic detection use a uniform reasoning strategy . existing approaches lack a framework to deal with the diverse analytical demands of sarcasm . |
| Approach: | They propose a Retrieval-Augmented Multi-Agent framework for Sarcasm Detection . the framework provides transparent and interpretable reasoning traces . |
| Outcome: | The proposed framework outperforms existing methods on four benchmarks and outperformed the strong GPT-4o+CoC baseline. |