Masader: Metadata Sourcing for Arabic Text and Speech Data Resources

By Zaid Alyafeai, Maraim Masoud, Mustafa Ghaleb, Maged Saeed Al-shaibani. 2021

36 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022, background): “Complementary to those efforts, other working group participants compiled language-specific resources such as the Arabic-focused Masader repository (Alyafeai et al., 2021; Altaher et al., 2022).”
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset (2023, background): “Arabic language resources in the Masader repository (Alyafeai et al., 2021), and proposed a list of websites of interest to increase the geographical diversity of our English, Spanish, and Chinese language data.”
Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources (2022, background): “Masader, developed by BigScience members prior to the organization of the hackathons, similarly provides metadata about Arabic-language NLP datasets without hosting the data (Alyafeai et al., 2021).”
Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model (2024, background): “(Mirzakhalov, 2021) for Turkic Languages (Mirzakhalov et al., 2021), IndicCorp and IndicXtream (Doddapaneni et al., 2023) for Indic languages, Masader (Alyafeai et al., 2021) for Arabic (Altaher et al., 2022) and SEACrowd for South East Asian languages.”
Masader Plus: A New Interface for Exploring +500 Arabic NLP Datasets (2022, methodology): “refer to the Masader paper (Alyafeai et al., 2021) for more information about the metadata.”