Comparison of Deep Learning Models for Automatic Detection of Sarcasm Context on the MUStARD Dataset

Many approaches have been proposed, from basic machine learning to multi-modal deep learning solutions, and progress has been made. Context has proven to be instrumental for sarcasm and many techniques that use context to identify sarcasm have emerged.

However, no NLP research has focused on sarcasm-context detection as the main topic. Therefore, this paper proposes an approach for the automatic detection of sarcasm context, aiming to develop models that can correctly identify the contexts in which sarcasm may occur or is appropriate. Using an established dataset, MUStARD, multiple models are trained and benchmarked to find the best performer for sarcasm-context detection.