| 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. |
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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. |
“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. |
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. |
Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for sarcasm detection are limited by supervised learning or prompt engineering . a new approach decomposes sarcasm detection into three dimensions: language, context, and emotion . |
| Approach: | They propose a method that decomposes sarcasm detection into three dimensions: language, context, and emotion. |
| Outcome: | The proposed method outperforms state-of-the-art methods in most cases. |
A Modular Architecture for Unsupervised Sarcasm Generation (D19-1)
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| 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. |
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. |
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues (2022.acl-long)
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| Challenge: | Indirect speech achieves a constellation of discourse goals in human communication, but it is challenging for AI agents to comprehend such idiosyncrasies. |
| Approach: | They propose a task to generate natural language explanations of satirical conversations using a multimodal and code-mixed dataset to capture multimodality. |
| Outcome: | The proposed task generates natural language explanations of satirical conversations in a multimodal and code-mixed setting and surpasses baselines on almost all metrics. |
LLMs in Sarcasm Detection? It’s elementary! (Or is it?) (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are often cited for their sophisticated pragmatic reasoning, but they collapse to random guessing on organic human speech. |
| Approach: | They propose that LLMs have near-human competence in sarcasm detection . authors propose that this proficiency may be deceptive . |
| Outcome: | The proposed model performance on synthetic leaderboards is a statistical mirage of competence. |
Representing Social Media Users for Sarcasm Detection (D18-1)
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| Challenge: | Existing annotated corpus of Reddit comments is limited by available annotation methods. |
| Approach: | They propose a Bayesian approach that directly represents authors’ propensities to be sarcastic and a dense embedding approach that can learn interactions between the author and the text. |
| Outcome: | The proposed approach performs better in homogeneous contexts, whereas the dense embeddings prove valuable in more diverse contexts. |
Generalizable Sarcasm Detection is Just Around the Corner, of Course! (2024.naacl-long)
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| Challenge: | sarcasm can be used to hurt, criticize, or deride but also to be mocking, humorous, or to bond. |
| Approach: | They tested the robustness of sarcasm detection models by fine-tuning their behavior on four sarkasmatic datasets . they found that models performed better when fine- tuned with third-party labels than with author labels. |
| Outcome: | The proposed models performed better when fine-tuned with third-party labels than with author labels on the same dataset and across different datasets. |