SarcasmDet at Sarcasm Detection Task 2021 in Arabic using AraBERT Pretrained Model

Dalya Faraj, Dalya Faraj, Malak Abdullah


Abstract
This paper presents one of the top five winning solutions for the Shared Task on Sarcasm and Sentiment Detection in Arabic (Subtask-1 Sarcasm Detection). The goal of the task is to identify whether a tweet is sarcastic or not. Our solution has been developed using ensemble technique with AraBERT pre-trained model. We describe the architecture of the submitted solution in the shared task. We also provide the experiments and the hyperparameter tuning that lead to this result. Besides, we discuss and analyze the results by comparing all the models that we trained or tested to achieve a better score in a table design. Our model is ranked fifth out of 27 teams with an F1 score of 0.5985. It is worth mentioning that our model achieved the highest accuracy score of 0.7830
Anthology ID:
2021.wanlp-1.44
Volume:
Proceedings of the Sixth Arabic Natural Language Processing Workshop
Month:
April
Year:
2021
Address:
Kyiv, Ukraine (Virtual)
Venue:
WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
345–350
Language:
URL:
https://aclanthology.org/2021.wanlp-1.44
DOI:
Bibkey:
Cite (ACL):
Dalya Faraj, Dalya Faraj, and Malak Abdullah. 2021. SarcasmDet at Sarcasm Detection Task 2021 in Arabic using AraBERT Pretrained Model. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 345–350, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
Cite (Informal):
SarcasmDet at Sarcasm Detection Task 2021 in Arabic using AraBERT Pretrained Model (Faraj et al., WANLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/nodalida-main-page/2021.wanlp-1.44.pdf
Data
ArSarcasm-v2