Sarcasm is a term that refers to the use of words to mock, irritate, or amuse
someone. It is commonly used on social media. The metaphorical and creative
nature of sarcasm presents a significant difficulty for sentiment analysis
systems based on affective computing. The methodology and results of our team,
UTNLP, in the SemEval-2022 shared task 6 on sarcasm detection are presented in
this paper. We put different models, and data augmentation approaches to the
test and report on which one works best. The tests begin with traditional
machine learning models and progress to transformer-based and attention-based
models. We employed data augmentation based on data mutation and data
generation. Using RoBERTa and mutation-based data augmentation, our best
approach achieved an F1-sarcastic of 0.38 in the competition's evaluation
phase. After the competition, we fixed our model's flaws and achieved an
F1-sarcastic of 0.414.Comment: 6 pages, 2 figures, International Workshop on Semantic Evaluation
co-located with NAAC