61 papers with code • 9 benchmarks • 13 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.
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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.
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.
UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation
Using RoBERTa and mutation-based data augmentation, our best approach achieved an F1-sarcastic of 0. 38 in the competition's evaluation phase.