A Mahalanobis-distance contrast loss plus a Beta-distribution decision rule lets small-parameter models match or beat larger LLMs on sexism, sarcasm, and metaphor detection with one epoch of training.
Sarcasm Detection using Hybrid Neural Network
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abstract
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.
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Class Distillation with Mahalanobis Contrast: An Efficient Training Paradigm for Pragmatic Language Understanding Tasks
A Mahalanobis-distance contrast loss plus a Beta-distribution decision rule lets small-parameter models match or beat larger LLMs on sexism, sarcasm, and metaphor detection with one epoch of training.