ALUE: Arabic Language Understanding Evaluation

Haitham Seelawi, Ibraheem Tuffaha, Mahmoud Gzawi, Wael Farhan, Bashar Talafha, Riham Badawi, Zyad Sober, Oday Al-Dweik, Abed Alhakim Freihat, Hussein Al-Natsheh


Abstract
The emergence of Multi-task learning (MTL)models in recent years has helped push thestate of the art in Natural Language Un-derstanding (NLU). We strongly believe thatmany NLU problems in Arabic are especiallypoised to reap the benefits of such models. Tothis end we propose the Arabic Language Un-derstanding Evaluation Benchmark (ALUE),based on 8 carefully selected and previouslypublished tasks. For five of these, we providenew privately held evaluation datasets to en-sure the fairness and validity of our benchmark. We also provide a diagnostic dataset to helpresearchers probe the inner workings of theirmodels.Our initial experiments show thatMTL models outperform their singly trainedcounterparts on most tasks. But in order to en-tice participation from the wider community,we stick to publishing singly trained baselinesonly. Nonetheless, our analysis reveals thatthere is plenty of room for improvement inArabic NLU. We hope that ALUE will playa part in helping our community realize someof these improvements. Interested researchersare invited to submit their results to our online,and publicly accessible leaderboard.
Anthology ID:
2021.wanlp-1.18
Volume:
Proceedings of the Sixth Arabic Natural Language Processing Workshop
Month:
April
Year:
2021
Address:
Kyiv, Ukraine (Virtual)
Editors:
Nizar Habash, Houda Bouamor, Hazem Hajj, Walid Magdy, Wajdi Zaghouani, Fethi Bougares, Nadi Tomeh, Ibrahim Abu Farha, Samia Touileb
Venue:
WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
173–184
Language:
URL:
https://aclanthology.org/2021.wanlp-1.18
DOI:
Bibkey:
Cite (ACL):
Haitham Seelawi, Ibraheem Tuffaha, Mahmoud Gzawi, Wael Farhan, Bashar Talafha, Riham Badawi, Zyad Sober, Oday Al-Dweik, Abed Alhakim Freihat, and Hussein Al-Natsheh. 2021. ALUE: Arabic Language Understanding Evaluation. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 173–184, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
Cite (Informal):
ALUE: Arabic Language Understanding Evaluation (Seelawi et al., WANLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2021.wanlp-1.18.pdf
Code
 Alue-Benchmark/alue_baselines
Data
Semantic Question Similarity in Arabic