SOTAVerified

Sarcasm Detection

The goal of Sarcasm Detection is to determine whether a sentence is sarcastic or non-sarcastic. Sarcasm is a type of phenomenon with specific perlocutionary effects on the hearer, such as to break their pattern of expectation. Consequently, correct understanding of sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and, frequently some real world facts.

Source: Attentional Multi-Reading Sarcasm Detection

Papers

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Showing 101–150 of 266 papers

TitleStatusHype
reamtchka at SemEval-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasetsCode0
R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm—0
stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets—0
GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian model for Irony Type Identification—0
Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation—0
TechSSN at SemEval-2022 Task 6: Intended Sarcasm Detection using Transformer Models—0
High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic texts—0
PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm—0
I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning Techniques—0
I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning—0
NULL at SemEval-2022 Task 6: Intended Sarcasm Detection Using Stylistically Fused Contextualized Representation and Deep Learning—0
TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection—0
Understanding the Sarcastic Nature of Emojis with SarcOji—0
ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight Models—0
UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection—0
JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-grams—0
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling—0
AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning TechniquesCode0
akaBERT at SemEval-2022 Task 6: An Ensemble Transformer-based Model for Arabic Sarcasm Detection—0
LISACTeam at SemEval-2022 Task 6: A Transformer based Approach for Intended Sarcasm Detection in English Tweets—0
LT3 at SemEval-2022 Task 6: Fuzzy-Rough Nearest Neighbor Classification for Sarcasm DetectionCode0
MarSan at SemEval-2022 Task 6: iSarcasm Detection via T5 and Sequence Learners—0
YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and Arabic—0
muBoost: An Effective Method for Solving Indic Multilingual Text Classification Problem—0
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and ArabicCode0
Baseline English and Maltese-English Classification Models for Subjectivity Detection, Sentiment Analysis, Emotion Analysis, Sarcasm Detection, and Irony Detection—0
BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts—0
Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource LanguageCode0
Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data AugmentationCode0
How Effective is Incongruity? Implications for Code-mix Sarcasm DetectionCode0
A Survey on Automated Sarcasm Detection on Twitter—0
Zombies Eat Brains, You are Safe: A Knowledge Infusion based Multitasking System for Sarcasm Detection in Meme—0
An Emoji-aware Multitask Framework for Multimodal Sarcasm Detection—0
Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets—0
How effective is incongruity? Implications for code-mixed sarcasm detectionCode0
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict—0
An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language IdentificationCode0
Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection—0
AraCOVID19-SSD: Arabic COVID-19 Sentiment and Sarcasm Detection Dataset—0
Does Commonsense help in detecting Sarcasm?Code0
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment ConflictCode0
Sarcasm Detection and Building an English Language Corpus in Real Time—0
Sarcasm Detection in Twitter -- Performance Impact while using Data Augmentation: Word EmbeddingsCode0
FiLMing Multimodal Sarcasm Detection with AttentionCode0
sarcasm detection and quantification in arabic tweets—0
Sarcasm Detection: A Comparative Study—0
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language—0
Parallel Deep Learning-Driven Sarcasm Detection from Pop Culture Text and English Humor Literature—0
¡Qué maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline—0
!Qué maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline—0
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All datasetsBIG-bench (SNARKS)FigLang 2020 Reddit DatasetFigLang 2020 Twitter DatasetSARC (all-bal)SARC (pol-bal)iSarcasmMUStARDSARC (pol-unbal)WITS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PaLM 2(few-shot, k=3, CoT)Accuracy84.8—Unverified
2PaLM 2 (few-shot, k=3, Direct)Accuracy78.7—Unverified
3PaLM 540B (few-shot, k=3)Accuracy78.1—Unverified
4BLOOM 176B (few-shot, k=3)Accuracy72.47—Unverified
5Bloomberg GPT (few-shot, k=3)Accuracy69.66—Unverified
6GPT-NeoX (few-shot, k=3)Accuracy62.36—Unverified
7Chinchilla-70B (few-shot, k=5)Accuracy58.6—Unverified
8Gopher-280B (few-shot, k=5)Accuracy48.3—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT+Aspect-based approachesF10.74—Unverified
2RoBERTa_large - (Separated Context-Response)F10.72—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa_large (Context-Response)F10.77—Unverified
2BERTF10.73—Unverified
#ModelMetricClaimedVerifiedStatus
1CASCADEAccuracy77—Unverified
2Bag-of-BigramsAccuracy75.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Bag-of-BigramsAccuracy76.5—Unverified
2CASCADEAccuracy74—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa + Mutation Data AugmentationF1-Score0.41—Unverified
#ModelMetricClaimedVerifiedStatus
1MUStARD++Precision70.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Bag-of-WordsAvg F127—Unverified
#ModelMetricClaimedVerifiedStatus
1BARTR136.88—Unverified