TY - GEN
T1 - SarcasmDet at SemEval-2022 Task 6
T2 - 16th International Workshop on Semantic Evaluation, SemEval 2022, co-located (hybrid) with The 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 2022
AU - Abdullah, Malak
AU - Faraj, Dalya
AU - Swedat, Safa
AU - Khrais, Jumana
AU - Al-Ayyoub, Mahmoud
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - 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 techniques. The paper describes the SarcasmDet 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).
AB - 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 techniques. The paper describes the SarcasmDet 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).
UR - https://www.scopus.com/pages/publications/85137607404
U2 - 10.18653/v1/2022.semeval-1.144
DO - 10.18653/v1/2022.semeval-1.144
M3 - Conference contribution
AN - SCOPUS:85137607404
T3 - SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop
SP - 1025
EP - 1030
BT - SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop
A2 - Emerson, Guy
A2 - Schluter, Natalie
A2 - Stanovsky, Gabriel
A2 - Kumar, Ritesh
A2 - Palmer, Alexis
A2 - Schneider, Nathan
A2 - Singh, Siddharth
A2 - Ratan, Shyam
PB - Association for Computational Linguistics (ACL)
Y2 - 14 July 2022 through 15 July 2022
ER -