natural-language-processing-10
Sarcasm Detection
3260 papers • 126 benchmarks • 313 datasets
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
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in sarcasm-detection-10
Trend
Dataset
Best Model
Actions
BIG-bench (SNARKS)
BIG-bench (SNARKS)
SARC (all-bal)
SARC (all-bal)
SARC (pol-bal)
Libraries
iUse these libraries to find sarcasm-detection-10 models and implementations
3 papers 31
Datasets
Subtasks
No subtasks available.
Most implemented papers
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Iyad Rahwan, Anders Søgaard, Bjarke Felbo, S. Lehmann, A. Mislove•Mon Jul 31 2017
This paper shows that by extending the distant supervision to a more diverse set of noisy labels, the models can learn richer representations and obtain state-of-the-art performance on 8 benchmark datasets within emotion, sentiment and sarcasm detection using a single pretrained model.
787
