Sarcasm Detection
63 papers with code • 9 benchmarks • 14 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.
Benchmarks
These leaderboards are used to track progress in Sarcasm Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Sarcasm Detection models and implementationsDatasets
Reddit
BIG-bench
SARC
iSarcasm
MUStARD++
Sarcasm Corpus V2
WITS
Latest papers
KoCoSa: Korean Context-aware Sarcasm Detection Dataset
In this paper, we introduce a new dataset for the Korean dialogue sarcasm detection task, KoCoSa (Korean Context-aware Sarcasm Detection Dataset), which consists of 12. 8K daily Korean dialogues and the labels for this task on the last response.
DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm Understanding
fesvhtr/DocMSU
•
•
Multimodal Sarcasm Understanding (MSU) has a wide range of applications in the news field such as public opinion analysis and forgery detection.
Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks
danaesavi/socialmedia-textimage-classification-auxlosses
•
•
However, prior work on multimodal classification of social media posts has not yet addressed these challenges.
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System
Multi-modal sarcasm detection has attracted much recent attention.
A big data approach towards sarcasm detection in Russian
We present a set of deterministic algorithms for Russian inflection and automated text synthesis.
Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification
Social media is daily creating massive multimedia content with paired image and text, presenting the pressing need to automate the vision and language understanding for various multimodal classification tasks.
DIP: Dual Incongruity Perceiving Network for Sarcasm Detection
The distribution is generated from the latest data stored in the memory bank, which can adaptively model the difference of semantic similarity between sarcastic and non-sarcastic data.
Finetuning for Sarcasm Detection with a Pruned Dataset
priyank96/dataset-pruning-sarcasm-detection
•
•
Sarcasm is usually conveyed through tone of voice, facial expressions, or other forms of nonverbal communication, but it can also be indicated by the use of certain words or phrases that are typically associated with irony or humor.
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues
LCS2-IIITD/MOSES
•
•
To this end, we explore the task of Sarcasm Explanation in Dialogues, which aims to unfold the hidden irony behind sarcastic utterances.
Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement
less-and-less-bugs/hkemodel
•
•
In this paper, we propose a novel hierarchical framework for sarcasm detection by exploring both the atomic-level congruity based on multi-head cross attention mechanism and the composition-level congruity based on graph neural networks, where a post with low congruity can be identified as sarcasm.
