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
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Libraries
Use these libraries to find Sarcasm Detection models and implementationsDatasets
Reddit
BIG-bench
SARC
iSarcasm
MUStARD++
Sarcasm Corpus V2
WITS
Most implemented papers
Deep and Dense Sarcasm Detection
Recent work in automated sarcasm detection has placed a heavy focus on context and meta-data.
A Transformer-based approach to Irony and Sarcasm detection
lorenzofamiglini/Irony-Sarcasm-Detection-Task
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Figurative Language (FL) seems ubiquitous in all social-media discussion forums and chats, posing extra challenges to sentiment analysis endeavors.
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour
To demonstrate the efficacy of this multi-task learning based approach to automatic essay grading, we collect gaze behaviour for 48 essays across 4 essay sets, and learn gaze behaviour for the rest of the essays, numbering over 7000 essays.
Reactive Supervision: A New Method for Collecting Sarcasm Data
Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data.
"Did you really mean what you said?" : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word Embeddings
With the increased use of social media platforms by people across the world, many new interesting NLP problems have come into existence.
Affective and Contextual Embedding for Sarcasm Detection
nastaranba/ace-for-sarcasm-detection
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COLING 2020
To the best of our knowledge, this is the first attempt to directly alter BERT{'}s architecture and train it from scratch to build a sarcasm classifier.
"Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space
ritvikshrivastava/multitask_transformers
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We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e. g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures).
``Laughing at you or with you'': The Role of Sarcasm in Shaping the Disagreement Space
ritvikshrivastava/multitask_transformers
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EACL 2021
We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e. g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures).
Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection
The existence of multiple datasets for sarcasm detection prompts us to apply transfer learning to exploit their commonality.
Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations
In this work, we make two major contributions considering the above limitations: (1) we develop a Hindi-English code-mixed dataset, MaSaC, for the multi-modal sarcasm detection and humor classification in conversational dialog, which to our knowledge is the first dataset of its kind; (2) we propose MSH-COMICS, a novel attention-rich neural architecture for the utterance classification.
