AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques

Aya Lotfy, Marwan Torki, Nagwa El-Makky


Abstract
Sarcasm detection is an important task in Natural Language Understanding. Sarcasm is a form of verbal irony that occurs when there is a discrepancy between the literal and intended meanings of an expression. In this paper, we use the tweets of the Arabic dataset provided by SemEval-2022 task 6 to train deep learning classifiers to solve the sub-tasks A and C associated with the dataset. Sub-task A is to determine if the tweet is sarcastic or not. For sub-task C, given a sarcastic text and its non-sarcastic rephrase, i.e. two texts that convey the same meaning, determine which is the sarcastic one. In our solution, we utilize fine-tuned MARBERT (Abdul-Mageed et al., 2021) model with an added single linear layer on top for classification. The proposed solution achieved 0.5076 F1-sarcastic in Arabic sub-task A, accuracy of 0.7450 and F-score of 0.7442 in Arabic sub-task C. We achieved the 2nd and the 9th places for Arabic sub-tasks A and C respectively.
Anthology ID:
2022.semeval-1.125
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
891–895
Language:
URL:
https://aclanthology.org/2022.semeval-1.125
DOI:
10.18653/v1/2022.semeval-1.125
Bibkey:
Cite (ACL):
Aya Lotfy, Marwan Torki, and Nagwa El-Makky. 2022. AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 891–895, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques (Lotfy et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.125.pdf
Video:
 https://aclanthology.org/2022.semeval-1.125.mp4
Code
 ayalotfy/isarcasmeval