AfroLID: A Neural Language Identification Tool for African Languages

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1958 - 1981 December 7-11, 2022 ©2022 Association for Computational Linguistics AfroLID: A Neural Language Identification Tool for African Languages Ife Adebara1,⋆ AbdelRahim Elmadany1,⋆ Muhammad Abdul-Mageed1,2 Alcides Alcoba Inciarte1 1Deep Learning & Natural Language Processing Group, The University of British Columbia 2Department of Natural Language Processing & Department of Machine Learning, MBZUAI {ife.adebara@,a.elmadany@,muhammad.mageed@,alcobaaj@mail.}ubc.ca Abstract Language identification (LID) is a crucial pre- cursor for NLP, especially for mining web data. Problematically, most of the world’s 7000+ languages today are not covered by LID technologies. We address this pressing issue for Africa by introducing AfroLID, a neural LID toolkit for 517 African languages and varieties. AfroLID exploits a multi-domain web dataset manually curated from across 14 language families utilizing five orthographic systems. When evaluated on our blind Test set, AfroLID achieves 95.89 F1-score. We also compare AfroLID to five existing LID tools that each cover a small number of African languages, finding it to outperform them on most languages. We further show the util- ity of AfroLID in the wild by testing it on the acutely under-served Twitter domain. Fi- nally, we offer a number of controlled case studies and perform a linguistically-motivated error analysis that allow us to both show- case AfroLID’s powerful capabilities and limi- tations.1 1 Introduction Language identification (LID) is the task of identi- fying the human language a piece of text or speech segment belongs to. The proliferation of social media have allowed greater access to multilingual data, making automatic LID an important first step in processing human language appropriately (Tjan- dra et al., 2021; Thara and Poornachandran, 2021). This includes applications in speech, sign language, handwritten text, and other modalities of language. It also includes distinguishing languages in code- mixed datasets (Abdul-Mageed et al., 2020; Thara and Poornachandran, 2021). Unfortunately, for the majority of languages in the world, including most African languages, we do not have the resources for developing LID tools. ⋆ Authors contributed equally. 1AfroLID is publicly available at https://github.com/UBC- NLP/afrolid. Figure 1: All 50 African countries in our data, with our 517 languages/language varieties in colored circles overlayed within respective countries. More details are in Appendix E. This situation has implications for the future NLP technologies. For instance, LID has facili- tated development of widely multilingual models such mT5 (Xue et al., 2021) and large multilin- gual datasets such as CCAligned (El-Kishky et al., 2020), ParaCrawl (Esplà et al., 2019), WikiMa- trix (Schwenk et al., 2021), OSCAR (Ortiz Suárez et al., 2020), and mC4 (Xue et al., 2021) which have advanced research in NLP. Comparable re- sources are completely unavailable for the major- ity of the world’s 7000+ today, with only poor coverage of the so-called low-resource languages (LR). This is partly due to absence of LID tools, and impedes future NLP progress on these lan- guages (Adebara and Abdul-Mageed, 2022). The state of African languages is not any better than other regions: Kreutzer et al. (2021) perform a man- …

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