Sarcasm Detection
75 papers with code • 9 benchmarks • 15 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++
WITS
Sarcasm Corpus V2
Most implemented papers
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
bfelbo/deepmoji
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EMNLP 2017
NLP tasks are often limited by scarcity of manually annotated data.
A Large Self-Annotated Corpus for Sarcasm
We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for sarcasm research and for training and evaluating systems for sarcasm detection.
Modelling Context with User Embeddings for Sarcasm Detection in Social Media
We introduce a deep neural network for automated sarcasm detection.
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
Sarcasm detection is a key task for many natural language processing tasks.
Sarcasm Detection using Hybrid Neural Network
rishabhmisra/Sarcasm-Detection-using-CNN
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Sarcasm Detection has enjoyed great interest from the research community, however the task of predicting sarcasm in a text remains an elusive problem for machines.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Context-Dependent Sentiment Analysis in User-Generated Videos
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos.
The Role of Conversation Context for Sarcasm Detection in Online Interactions
To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the sarcastic response.
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection
Social media platforms like twitter and facebook have be- come two of the largest mediums used by people to express their views to- wards different topics.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
