Masader: Metadata Sourcing for Arabic Text and Speech Data Resources
This addresses the problem of dataset accessibility and metadata scarcity for researchers and practitioners in Arabic NLP, particularly for low-resource dialects, though it is incremental as it builds on existing cataloguing efforts.
The authors tackled the lack of metadata and public catalogues for Arabic NLP datasets by creating Masader, the largest public catalogue for Arabic NLP datasets, which includes 200 datasets annotated with 25 attributes, and developed an extensible metadata annotation strategy.
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 \textit{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.