Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language

Abdelkader El Mahdaouy, Abdellah El Mekki, Kabil Essefar, Nabil El Mamoun, Ismail Berrada, Ahmed Khoumsi


Abstract
The prominence of figurative language devices, such as sarcasm and irony, poses serious challenges for Arabic Sentiment Analysis (SA). While previous research works tackle SA and sarcasm detection separately, this paper introduces an end-to-end deep Multi-Task Learning (MTL) model, allowing knowledge interaction between the two tasks. Our MTL model’s architecture consists of a Bidirectional Encoder Representation from Transformers (BERT) model, a multi-task attention interaction module, and two task classifiers. The overall obtained results show that our proposed model outperforms its single-task and MTL counterparts on both sarcasm and sentiment detection subtasks.
Anthology ID:
2021.wanlp-1.42
Volume:
Proceedings of the Sixth Arabic Natural Language Processing Workshop
Month:
April
Year:
2021
Address:
Kyiv, Ukraine (Virtual)
Venues:
EACL | WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
334–339
Language:
URL:
https://aclanthology.org/2021.wanlp-1.42
DOI:
Bibkey:
Cite (ACL):
Abdelkader El Mahdaouy, Abdellah El Mekki, Kabil Essefar, Nabil El Mamoun, Ismail Berrada, and Ahmed Khoumsi. 2021. Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 334–339, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
Cite (Informal):
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language (El Mahdaouy et al., WANLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wanlp-1.42.pdf
Data
ArSarcasm-v2