@inproceedings{el-mahdaouy-etal-2021-deep,
title = "Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in {A}rabic Language",
author = "El Mahdaouy, Abdelkader and
El Mekki, Abdellah and
Essefar, Kabil and
El Mamoun, Nabil and
Berrada, Ismail and
Khoumsi, Ahmed",
editor = "Habash, Nizar and
Bouamor, Houda and
Hajj, Hazem and
Magdy, Walid and
Zaghouani, Wajdi and
Bougares, Fethi and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Touileb, Samia",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wanlp-1.42",
pages = "334--339",
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.",
}
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%0 Conference Proceedings
%T Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language
%A El Mahdaouy, Abdelkader
%A El Mekki, Abdellah
%A Essefar, Kabil
%A El Mamoun, Nabil
%A Berrada, Ismail
%A Khoumsi, Ahmed
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Hajj, Hazem
%Y Magdy, Walid
%Y Zaghouani, Wajdi
%Y Bougares, Fethi
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Touileb, Samia
%S Proceedings of the Sixth Arabic Natural Language Processing Workshop
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv, Ukraine (Virtual)
%F el-mahdaouy-etal-2021-deep
%X 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.
%U https://aclanthology.org/2021.wanlp-1.42
%P 334-339
Markdown (Informal)
[Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language](https://aclanthology.org/2021.wanlp-1.42) (El Mahdaouy et al., WANLP 2021)
ACL