Masader: Metadata Sourcing for Arabic Text and Speech Data Resources

Zaid Alyafeai, Maraim Masoud, Mustafa Ghaleb, Maged S. Al-shaibani


Abstract
The NLP pipeline has evolved dramatically in the last few years. The first step in the pipeline is to find suitable annotated datasets to evaluate the tasks we are trying to solve. Unfortunately, most of the published datasets lack metadata annotations that describe their attributes. Not to mention, the absence of a public catalogue that indexes all the publicly available datasets related to specific regions or languages. When we consider low-resource dialectical languages, for example, this issue becomes more prominent. In this paper, we create Masader, the largest public catalogue for Arabic NLP datasets, which consists of 200 datasets annotated with 25 attributes. Furthermore, we develop a metadata annotation strategy that could be extended to other languages. We also make remarks and highlight some issues about the current status of Arabic NLP datasets and suggest recommendations to address them.
Anthology ID:
2022.lrec-1.681
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6340–6351
Language:
URL:
https://aclanthology.org/2022.lrec-1.681
DOI:
Bibkey:
Cite (ACL):
Zaid Alyafeai, Maraim Masoud, Mustafa Ghaleb, and Maged S. Al-shaibani. 2022. Masader: Metadata Sourcing for Arabic Text and Speech Data Resources. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6340–6351, Marseille, France. European Language Resources Association.
Cite (Informal):
Masader: Metadata Sourcing for Arabic Text and Speech Data Resources (Alyafeai et al., LREC 2022)
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PDF:
https://aclanthology.org/2022.lrec-1.681.pdf