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This paper describes our submission to SemEval-2022 Task 6 on sarcasmdetection and its five subtasks for English and Arabic. Sarcasm conveys ameaning which contradicts the literal meaning, and it is mainly found on socialnetworks. It has a significant role in understanding the intention of the user.For detecting sarcasm, we used deep learning techniques based on transformersdue to its success in the field of Natural Language Processing (NLP) withoutthe need for feature engineering. The datasets were taken from tweets. Wecreated new datasets by augmenting with external data or by using wordembeddings and repetition of instances. Experiments were done on the datasetswith different types of preprocessing because it is crucial in this task. Therank of our team was consistent across four subtasks (fourth rank in threesubtasks and sixth rank in one subtask); whereas other teams might be in thetop ranks for some subtasks but rank drastically less in other subtasks. Thisimplies the robustness and stability of the models and the techniques we used.

Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation