Papers with sarcasm detection
Towards Code-switched Classification Exploiting Constituent Language Resources (2020.aacl-srw)
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| Challenge: | Code-switching is a communicative phenomenon denoting a shift from one language to another within the same speech exchange. |
| Approach: | They propose to convert code-switched data into its constituent high resource languages for use in both monolingual and cross-lingual settings. |
| Outcome: | The proposed code-switching language can be used for multiple downstream tasks . the proposed language increases the F1 score by 22% and 42.5% compared to the state-of-the-art. |
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. |
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes (2020.aacl-main)
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| Challenge: | Empirical results show the efficacy of our proposed multi-task framework over existing state-of-the-art systems. |
| Approach: | They propose a multi-task, multi-modal deep learning framework to solve multiple tasks simultaneously. |
| Outcome: | The proposed framework performs better than existing state-of-the-art systems on a complicated form of information, i.e., memes. |
Affective Retrofitted Word Embeddings (2022.aacl-main)
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| Challenge: | Word embeddings do not capture affective dimensions of valence, arousal, and dominance . valency, valance, and adolescence are present in words, but are not represented in text . |
| Approach: | They propose a method for updating word embeddings for affective meaning . they use a non-linear transformation function that maps pre-trained embedders to an affective vector space . |
| Outcome: | The proposed method improves inter-cluster and intra-c cluster distances for emotion-bearing words. |
Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks (2024.findings-eacl)
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| Challenge: | Prior work on multimodal content classification has not addressed these challenges. |
| Approach: | They propose to use two auxiliary tasks to fine-tune multimodal models to address hidden cross-modal semantics and weak image-text relationships when modeling text and images. |
| Outcome: | The proposed model improves by up to 2.6 F1 score across five diverse social media datasets. |
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. |
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict (2022.findings-naacl)
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| Challenge: | Sarcasm employs ambivalence, where one says something positive but actually means negative . linguistically, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit . |
| Approach: | They propose a Dual-Channel Framework to model literal and implied sentiments separately . they propose sarcastic networks that can detect sarcasm sentiments in political debates . |
| Outcome: | The proposed framework achieves state-of-the-art on political debates and Twitter datasets. |
CASCADE: Contextual Sarcasm Detection in Online Discussion Forums (C18-1)
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| Challenge: | Existing studies on sarcasm detection focus on lexical, syntactic and semantic cues, but sarcasm can be expressed implicitly without such cue. |
| Approach: | They propose a ContextuAl SarCasm DEtector which extracts contextual information from the discourse of a discussion thread. |
| Outcome: | The proposed model improves on a large Reddit corpus. |
“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space (2021.eacl-main)
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| Challenge: | Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. |
| Approach: | They propose to use a corpus annotated with argumentative moves and sarcasm to model sarcastic relationships using deep learning architectures. |
| Outcome: | The proposed setup improves the argumentative relation classification task using deep learning architectures. |
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning (2023.emnlp-main)
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| Challenge: | a novel retrofitting method to induce emotion aspects into pre-trained language models is proposed . the models are computationally less expensive and open, but do not capture affective aspects of human communication well. |
| Approach: | They propose a retrofitting method to induce emotion aspects into pre-trained language models . they retrofit text fragments exhibiting similar emotions into pretrained networks . |
| Outcome: | The proposed method produces emotion-aware text representations for sentiment analysis and sarcasm detection tasks. |
Breakthrough from Nuance and Inconsistency: Enhancing Multimodal Sarcasm Detection with Context-Aware Self-Attention Fusion and Word Weight Calculation. (2024.lrec-main)
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| Challenge: | Existing methods for sarcasm detection rely on feature concatenation to fuse different modalities or model inconsistencies among modalités. |
| Approach: | They propose to use Context-Aware Self-Attention Fusion to integrate local and momentary multimodal information into specific words to illustrate the inconsistencies between connotation and denotation. |
| Outcome: | The proposed method achieves an accuracy of 76.9 and an F1 score of 76.1 on the MUStARD dataset, surpassing the current state-of-the-art IWAN model by 1.7 and 1.6 respectively. |
Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model (P19-1)
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| Challenge: | Existing methods to detect sarcasm focus on text, but they are insufficient for multi-modal messages. |
| Approach: | They propose a multi-modal hierarchical sarcasm detection model for tweets consisting of texts and images in Twitter. |
| Outcome: | The proposed model is able to detect sarcasm on twitter using three modalities . the proposed model can be used in customer service, opinion mining and harassment detection . |
Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement (2022.emnlp-main)
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| Challenge: | Sarcasm is a linguistic phenomenon indicating a discrepancy between literal meanings and implied intentions. |
| Approach: | They propose a hierarchical framework for sarcasm detection by exploring atomic-level congruity and composition-level convergence. |
| Outcome: | The proposed model outperforms existing methods on a public sarcasm detection dataset based on Twitter . |
Emotion Enriched Retrofitted Word Embeddings (2022.coling-1)
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| Challenge: | Word embeddings that encode lexical-semantic relations do not capture emotion aspects of words. |
| Approach: | They propose a retrofitting method to update the vectors of emotion bearing words . they find that the retrofitted embeddings achieve better distances between clusters . |
| Outcome: | The proposed method achieves better distances between clusters and clusters for words having the same emotions. |
Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification (2022.emnlp-main)
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| Challenge: | Social media users are using images and text to voice opinions and share ideas. |
| Approach: | They propose to use user comments to extract hinting features from user comments and explore them via self-training. |
| Outcome: | The proposed framework improves on four social media benchmarks for image-text relation classification, sarcasm detection, sentiment classification, and hate speech detection. |
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. |
Cross-Cultural Transfer Learning for Text Classification (D19-1)
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| Challenge: | a large dataset is required to achieve competitive performance in most natural language tasks. large datasets are expensive, time consuming, and error-prone. |
| Approach: | They propose a transfer-learning framework that leverages bilingual corpora for natural language text classification using no task-specific data. |
| Outcome: | The proposed framework can achieve good performance on formality classification and sarcasm detection tasks without any task-specific labeled data. |
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis (2020.acl-main)
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| Challenge: | Existing systems for sarcasm detection are limited by the use of sarcasm . sarasm is often used to convey thinly veiled disapproval humorously. |
| Approach: | They propose a multi-task deep learning framework to solve sarcasm problems simultaneously . they manually annotate a sarcsm dataset with sentiment and emotion classes . |
| Outcome: | The proposed framework is able to solve sarcasm, sentiment and emotion problems in a multi-modal conversational scenario. |
Sarcasm Target Identification: Dataset and An Introductory Approach (L18-1)
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| 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. |
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English (2025.findings-acl)
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| Challenge: | despite large language models showing bias against non-mainstream varieties, there are no labeled datasets for sentiment analysis of English. |
| Approach: | They propose a benchmark for sentiment and sarcasm classification for three varieties of English . they manually annotate the datasets with sentiment and the sarcasmatic labels . |
| Outcome: | The proposed benchmark is based on a web-based content from Google Place reviews and Reddit comments. |
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper) (P19-1)
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Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria
| Challenge: | sarcasm is often expressed through multiple verbal and non-verbal cues, such as a change of tone, overemphasis, drawn-out syllables, or a straight looking face. |
| Approach: | They propose to use multimodal cues to improve sarcasm detection using audiovisual utterances annotated with sarcasm labels to improve the accuracy. |
| Outcome: | The proposed dataset reduces the error rate of sarcasm detection by 12.9% . it is based on audiovisual utterances annotated with sarcasm labels . |
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks (2020.acl-main)
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| Challenge: | Affective tasks such as sentiment analysis, emotion classification and sarcasm detection have enjoyed great popularity in recent years. |
| Approach: | They conduct a comprehensive analysis of the role of preprocessing techniques in affective analysis based on word vector models. |
| Outcome: | The proposed model is the first of its kind and provides useful insights on the role of each preprocessing technique when applied at the training phase, commonly ignored in pretrained word vector models, and/or at the downstream task phase. |
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. |
Urban Dictionary Embeddings for Slang NLP Applications (2020.lrec-1)
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| Challenge: | a new set of word embeddings is released to improve word embedment performance . word embeds provide useful representations of meanings of words in vectors . |
| Approach: | They present a set of word embeddings trained on Urban Dictionary . they show they have high performance across a range of common word embeding evaluations . |
| Outcome: | The first set of word embeddings trained on Urban Dictionary has high performance . the embeddables perform better on a range of common word evaluation tasks . |
Rhetorical Device-Aware Sarcasm Detection with Counterfactual Data Augmentation (2025.findings-acl)
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| Challenge: | Sarcasm is a complex form of sentiment expression widely used in human daily life. |
| Approach: | They propose a device-aware sarcasm dataset with counterfactually augmented data to capture its complexity. |
| Outcome: | The proposed dataset shows that it is more balanced than zero-shot models. |
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. |
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing methods treat three modal features equally, without distinguishing the importance of different modalities. Existing models split the video into frames, leading to missing the global acoustic information. |
| Approach: | They propose a global Acoustic feature enhanced Modal-Order-Aware network to address these problems. |
| Outcome: | The proposed model outperforms state-of-the-art models on two public datasets. |
A Multimodal Corpus for Emotion Recognition in Sarcasm (2022.lrec-1)
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| 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. |
InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks (2024.lrec-main)
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| Challenge: | InfFeed uses influence functions to compute the influential instances for a target instance. |
| Approach: | They propose an apparatus that uses influence functions to compute the influential instances for a target instance. |
| Outcome: | The proposed model outperforms the state-of-the-art baselines by 4% for hate speech classification, 3.5% for stance classification, and 3% for irony and 2% for sarcasm detection. |
Predict and Use: Harnessing Predicted Gaze to Improve Multimodal Sarcasm Detection (2023.emnlp-main)
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| Challenge: | sarcasm detection depends on content spoken, tonality, facial expressions, context, and personal traits like language proficiency and cognitive capabilities. |
| Approach: | They propose to use synthetic gaze data to improve sarcasm detection in conversational context . they collect gaze features for 20% of data instances and use them to predict gaze features . |
| Outcome: | The proposed model improves performance on a conversational dataset using gaze features . it achieves a gain of 6.6% points on the complete dataset with only predicted gaze features. |
Sarcasm Detection in a Disaster Context (2024.lrec-main)
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| Challenge: | During natural disasters, people often use social media platforms to express contempt or sarcasm . despite being widely researched as an NLP task, sarkasmatic detection has not been explored in a specific context . |
| Approach: | They propose a dataset of 15,000 tweets annotated for intended sarcasm . they propose sarkasmatic detection using pre-trained language models . |
| Outcome: | The proposed model can obtain as much as 0.70 F1 on the dataset. |
SarcNet: A Multilingual Multimodal Sarcasm Detection Dataset (2024.lrec-main)
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| Challenge: | Sarcasm is an implicit form of sarcasm, involving an intended meaning that contradicts the literal expression . human use conflict between factual information and a statement as cues to detect sarcasm . sarkasmatic analysis is challenging due to its implicit nature . |
| Approach: | They propose a multimodal sarcasm detection dataset that uses multiple modalities to detect sarcasm. |
| Outcome: | The proposed model improves on previous models based on a single label . human sarcasm cannot be detected using a unified label across multiple modalities . |
Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection (2025.findings-emnlp)
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| Challenge: | Specifically, we tried native sample mixing, multi-task learning, and prompting and instruction finetuning very large multilingual language models (VMLMs). |
| Approach: | They used native sample mixing, multi-task learning and prompting and instruction finetuning to improve code-mixed humour and sarcasm detection. |
| Outcome: | The proposed methods improve humour and sarcasm detection by adding native samples to training sets and multitask learning and prompting and instruction finetuning VMLMs. |
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. |