| Challenge: | Past work on sarcasm detection has focused on identifying the sarcasm target of ridicule in a sarkastic text. |
| Approach: | They propose a task of extracting the sarcastic target of ridicule from a sarcastical text using a manually annotated dataset and an automatic approach. |
| Outcome: | The proposed approach establishes the viability of sarcasm target identification and will serve as a baseline for future work. |
Similar Papers
A deep-learning framework to detect sarcasm targets (D19-1)
Copied to clipboard
| Challenge: | Existing methods for sarcasm target detection are difficult to implement in natural language processing. |
| Approach: | They propose a deep learning framework for sarcasm target detection in predefined sarkastic texts. |
| Outcome: | The proposed framework improves accuracy and accuracy in match and dice scores compared to the current state-of-the-art framework. |
The Design and Construction of a Chinese Sarcasm Dataset (2020.lrec-1)
Copied to clipboard
| 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. |
iSarcasm: A Dataset of Intended Sarcasm (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for detecting intended sarcasm have shown low performance compared to previous studies. |
| Approach: | They propose a dataset of tweets labeled for intended sarcasm by their authors . they aim to encourage future NLP research to develop methods for detecting sarkasmus in text as intended by the authors of the text . |
| Outcome: | The proposed model shows that existing methods are biased or obvious and sarcasm could be understudied. |
Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection (2022.findings-emnlp)
Copied to clipboard
| Challenge: | sarcasm detection datasets focus on intended, rather than perceived sarcasm, but there is no comparison between human and machine performance. |
| Approach: | They collect author-annotated sarcasm datasets that focus on intended, rather than perceived sarcasticism . they compare human-level benchmarks to that of state-of-the-art sarkasmatic detection systems . |
| Outcome: | The proposed datasets compare human and machine performance on sarcastic tasks in English and Arabic. |
Representing Social Media Users for Sarcasm Detection (D18-1)
Copied to clipboard
| 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. |
Exploring Author Context for Detecting Intended vs Perceived Sarcasm (P19-1)
Copied to clipboard
| Challenge: | Existing studies on textual sarcasm detection use manual labelling and tag-based distant supervision to detect sarcasm. |
| Approach: | They define author context as the embedded representation of their historical tweets and suggest neural models that extract these representations. |
| Outcome: | The proposed models achieve state-of-the-art on two datasets labelled manually and via tag-based distant supervision indicating a difference between intended and perceived sarcasm . |
Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning (2025.findings-emnlp)
Copied to clipboard
| 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. |
Generalizable Sarcasm Detection is Just Around the Corner, of Course! (2024.naacl-long)
Copied to clipboard
| 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. |
A Multimodal Corpus for Emotion Recognition in Sarcasm (2022.lrec-1)
Copied to clipboard
| Challenge: | sarcasm and emotion are often used in conversational systems to generate the right response. |
| Approach: | They use a sarcastic expression dataset pre-annotated with 9 emotions to detect emotion . they identify and correct 343 incorrect emotion labels and label each sarkastic utterance with one of four sarcasm types. |
| Outcome: | The proposed model outperforms state-of-the-art sarcasm detection methods by using a multimodal sarcastic detection dataset. |
What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (D19-55)
Copied to clipboard
| Challenge: | Existing datasets for irony detection only contain 10% of ironic tweets with emojis . 45% of internet users in the united states use an e-moji in social media . |
| Approach: | They propose to use emojis to analyze irony detection datasets to train classifiers. |
| Outcome: | The proposed pipeline can be used to analyze irony detection datasets using emojis. |