SarcasmDet at SemEval-2022 Task 6: Detecting Sarcasm using Pre-trained Transformers in English and Arabic Languages

Malak Abdullah, Dalya Alnore, Safa Swedat, Jumana Khrais, Mahmoud Al-Ayyoub
2022 Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)   unpublished
This paper presents solution systems for task 6 at SemEval2022, iSarcasmEval: Intended Sarcasm Detection In English and Arabic. The shared task 6 consists of three sub-task. We participated in subtask A for both languages, Arabic and English. The goal of subtask A is to predict if a tweet would be considered sarcastic or not. The proposed solution SarcasmDet has been developed using the state-of-the-art Arabic and English pre-trained models AraBERT, MARBERT, BERT, and RoBERTa with ensemble
more » ... iques. The paper describes the Sarcas-mDet architecture with the fine-tuning of the best hyperparameter that led to this superior system. Our model ranked seventh out of 32 teams in subtask A-Arabic with an f1-sarcastic of 0.4305 and Seventeen out of 42 teams with f1-sarcastic 0.3561. However, we built another model to score f-1 sarcastic with 0.43 in English after the deadline. Both Models (Arabic and English scored 0.43 as f-1 sarcastic with ranking seventh).
doi:10.18653/v1/2022.semeval-1.144 fatcat:4cejju4szja53fiu44hrzobz34