Challenge: a new study examines the recognition of irony by humans and automatic systems . a fine-grained annotation scheme allows for improved modeling of ironity in automatic systems.
Approach: They propose a fine-grained annotation scheme that allows for better recognition of irony by humans and automatic systems.
Outcome: The proposed model improves on tweets annotated with high confidence and agreement . it also performs better on high-confidence and highagreement samples compared to automated systems .

Similar Papers

Marking Irony Activators in a Universal Dependencies Treebank: The Case of an Italian Twitter Corpus (2020.lrec-1)

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Challenge: Existing annotations for irony are difficult, and the recognition of it is difficult due to its polarity.
Approach: They propose a fine-grained annotation scheme centered on irony that highlights the tokens responsible for its activation and their morpho-syntactic features.
Outcome: The proposed scheme highlights the tokens responsible for irony activation and their morpho-syntactic features.
A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives (2022.coling-1)

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Challenge: figurative language research has focused on sarcasm and irony, but there is still a gap in the field.
Approach: They propose to review computational irony, cognitive science, and neural models of irony processing . they aim to encourage a balanced and equal research environment in figurative languages .
Outcome: The proposed multi-X irony processing perspectives will provide an overview of computational irony, insights from linguisic theory and cognitive science, and interactions with downstream NLP tasks.
Tackling Irony Detection using Ensemble Classifiers (2022.lrec-1)

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Challenge: Automated approaches to irony detection still fall short of what one would consider desirable performance.
Approach: They propose to use transformer-based approaches to automate irony detection in social media . they propose to augmentation training data to address the binary and fine-grained problem .
Outcome: The proposed methods improve performance over baselines and are not decisive for good results.
What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (D19-55)

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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.
I’m sure you’re a real scholar yourself: Exploring Ironic Content Generation by Large Language Models (2024.findings-emnlp)

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Challenge: Moreover, irony is highly subjective and can depend on various factors, such as social, cultural, or generational aspects.
Approach: They propose to fine-tune two large language models to generate ironic and non-ironic content and analyze their outputs from a linguistic perspective.
Outcome: The proposed models generate ironic and non-ironic responses to a given social media post and analyze their outputs from a linguistic perspective.
Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection (2022.findings-emnlp)

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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.
Decision Biases and Intent-Irony Decoupling in Large Language Models (2026.findings-acl)

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Challenge: Large Language Models (LLMs) exhibit impressive linguistic fluency, but it remains unclear whether they possess human-like Theory of Mind (ToM) or rely on statistical heuristics . a recent study examined the performance of LLMs against 300 human participants .
Approach: a study establishes a framework for large language models that modulates contextual contrast, linguistic cues, and cognitive mechanisms.
Outcome: a new evaluation framework compares ten state-of-the-art LLMs against 300 human participants . the framework systematically modulates contextual contrast, linguistic cues, and cognitive mechanisms .
Multilingual Irony Detection with Dependency Syntax and Neural Models (2020.coling-main)

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Challenge: Several semantic and syntactic devices can be used to express irony, causing the incongruity, determine the clash and play the role of irony triggers within a text.
Approach: They propose to exploit linguistic resources where syntax is annotated according to the Universal Dependencies scheme.
Outcome: The proposed method exploits linguistic resources where syntax is annotated according to the Universal Dependencies scheme.
iSarcasm: A Dataset of Intended Sarcasm (2020.acl-main)

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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.
EPIC: Multi-Perspective Annotation of a Corpus of Irony (2023.acl-long)

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Challenge: EPIC is the first annotated corpus for irony analysis based on data perspectivism . a recent trend in natural language processing (NLP) postulates that the disagreement among annotators in a language resource is a valuable source of knowledge, rather than noise that ought to be minimized or discarded.
Approach: They propose to annotate an English perspectivist irony corpus based on data perspectivism . they validate the model by creating perspective-aware models that encode the perspectives of annotators grouped according to their demographic characteristics.
Outcome: The proposed model can capture different perspectives on irony among different groups of annotators, and is more confident than non-perspectivist models.

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