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The first large scale collection of diverse Hausa language datasets

arXiv 2102.06991 ↗First posted 13 Feb 2021cs.CLcited by 6

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Abstract (arXiv)

Hausa language belongs to the Afroasiatic phylum, and with more first-language speakers than any other sub-Saharan African language. With a majority of its speakers residing in the Northern and Southern areas of Nigeria and the Republic of Niger, respectively, it is estimated that over 100 million people speak the language. Hence, making it one of the most spoken Chadic language. While Hausa is considered well-studied and documented language among the sub-Saharan African languages, it is viewed as a low resource language from the perspective of natural language processing (NLP) due to limited resources to utilise in NLP-related tasks. This is common to most languages in Africa; thus, it is crucial to enrich such languages with resources that will support and speed the pace of conducting various downstream tasks to meet the demand of the modern society. While there exist useful datasets, notably from news sites and religious texts, more diversity is needed in the corpus. We provide an expansive collection of curated datasets consisting of both formal and informal forms of the language from refutable websites and online social media networks, respectively. The collection is large and more diverse than the existing corpora by providing the first and largest set of Hausa social media data posts to capture the peculiarities in the language. The collection also consists of a parallel dataset, which can be used for tasks such as machine translation with applications in areas such as the detection of spurious or inciteful online content. We describe the curation process -- from the collection, preprocessing and how to obtain the data -- and proffer some research problems that could be addressed using the data.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 13 Feb 2021.

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6 of 13 references.

References · 6

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languagescited by 52
  3. 2020Masakhane -- Machine Translation For Africacited by 48
  4. 2019hauWE: Hausa Words Embedding for Natural Language Processingcited by 5
  5. 2020HausaMT v1.0: Towards English-Hausa Neural Machine Translationcited by 5
  6. 2020Towards Combating Pandemic-related Misinformation in Social Mediacited by 1

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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 3.

6 papers.