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 201–225 of 266 papers

TitleStatusHype
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)Code0
``When Numbers Matter!'': Detecting Sarcasm in Numerical Portions of Text—0
Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm DetectionCode0
Sentiment and Sarcasm Classification with Multitask Learning—0
Identifying Affective Events and the Reasons for their Polarity—0
Attentional Multi-Reading Sarcasm Detection—0
Representing Social Media Users for Sarcasm DetectionCode0
Sarcasm Analysis using Conversation Context—0
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining—0
#SarcasmDetection is soooo general! Towards a Domain-Independent Approach for Detecting Sarcasm—0
Detecting Sarcasm is Extremely Easy ;-)—0
NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in Tweets—0
YNU-HPCC at SemEval-2018 Task 3: Ensemble Neural Network Models for Irony Detection on Twitter—0
Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection—0
KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection—0
SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron—0
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm DetectionCode0
CASCADE: Contextual Sarcasm Detection in Online Discussion ForumsCode0
Reasoning with Sarcasm by Reading In-between—0
Facebook Reaction-Based Emotion Classifier as Cue for Sarcasm Detection—0
Sarcasm Target Identification: Dataset and An Introductory ApproachCode0
SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model—0
Towards Lower Bounds on Number of Dimensions for Word Embeddings—0
"Having 2 hours to write a paper is fun!": Detecting Sarcasm in Numerical Portions of Text—0
Computational Sarcasm—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