@inproceedings{khondaker-etal-2022-benchmark,
title = "A Benchmark Study of Contrastive Learning for {A}rabic Social Meaning",
author = "Khondaker, Md Tawkat Islam and
Nagoudi, El Moatez Billah and
Elmadany, AbdelRahim and
Abdul-Mageed, Muhammad and
Lakshmanan, V.S., Laks",
editor = "Bouamor, Houda and
Al-Khalifa, Hend and
Darwish, Kareem and
Rambow, Owen and
Bougares, Fethi and
Abdelali, Ahmed and
Tomeh, Nadi and
Khalifa, Salam and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wanlp-1.7",
doi = "10.18653/v1/2022.wanlp-1.7",
pages = "63--75",
abstract = "Contrastive learning (CL) has brought significant progress to various NLP tasks. Despite such a progress, CL has not been applied to Arabic NLP. Nor is it clear how much benefits it could bring to particular classes of tasks such as social meaning (e.g., sentiment analysis, dialect identification, hate speech detection). In this work, we present a comprehensive benchmark study of state-of-the-art supervised CL methods on a wide array of Arabic social meaning tasks. Through an extensive empirical analysis, we show that CL methods outperform vanilla finetuning on most of the tasks. We also show that CL can be data efficient and quantify this efficiency, demonstrating the promise of these methods in low-resource settings vis-a-vis the particular downstream tasks (especially label granularity).",
}
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%0 Conference Proceedings
%T A Benchmark Study of Contrastive Learning for Arabic Social Meaning
%A Khondaker, Md Tawkat Islam
%A Nagoudi, El Moatez Billah
%A Elmadany, AbdelRahim
%A Abdul-Mageed, Muhammad
%A Lakshmanan, V.S., Laks
%Y Bouamor, Houda
%Y Al-Khalifa, Hend
%Y Darwish, Kareem
%Y Rambow, Owen
%Y Bougares, Fethi
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Khalifa, Salam
%Y Zaghouani, Wajdi
%S Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F khondaker-etal-2022-benchmark
%X Contrastive learning (CL) has brought significant progress to various NLP tasks. Despite such a progress, CL has not been applied to Arabic NLP. Nor is it clear how much benefits it could bring to particular classes of tasks such as social meaning (e.g., sentiment analysis, dialect identification, hate speech detection). In this work, we present a comprehensive benchmark study of state-of-the-art supervised CL methods on a wide array of Arabic social meaning tasks. Through an extensive empirical analysis, we show that CL methods outperform vanilla finetuning on most of the tasks. We also show that CL can be data efficient and quantify this efficiency, demonstrating the promise of these methods in low-resource settings vis-a-vis the particular downstream tasks (especially label granularity).
%R 10.18653/v1/2022.wanlp-1.7
%U https://aclanthology.org/2022.wanlp-1.7
%U https://doi.org/10.18653/v1/2022.wanlp-1.7
%P 63-75
Markdown (Informal)
[A Benchmark Study of Contrastive Learning for Arabic Social Meaning](https://aclanthology.org/2022.wanlp-1.7) (Khondaker et al., WANLP 2022)
ACL
- Md Tawkat Islam Khondaker, El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed, and Laks Lakshmanan, V.S.. 2022. A Benchmark Study of Contrastive Learning for Arabic Social Meaning. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 63–75, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.