Sarcasm Detection using Hybrid Neural Network
This work addresses the problem of noisy datasets and limited insights in sarcasm detection for NLP researchers, but it is incremental as it builds on existing neural methods with a modest performance gain.
The paper tackled sarcasm detection in text by introducing a new dataset from sarcastic and real news websites and proposing a hybrid neural network with attention, resulting in a ~5% improvement in classification accuracy over the baseline.
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. Past studies mostly make use of twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of labels and language. To overcome these shortcoming, we introduce a new dataset which contains news headlines from a sarcastic news website and a real news website. Next, we propose a hybrid Neural Network architecture with attention mechanism which provides insights about what actually makes sentences sarcastic. Through experiments, we show that the proposed model improves upon the baseline by ~ 5% in terms of classification accuracy.