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# Towards Afrocentric NLP for African Languages:
Where We Are and Where We Can Go

Ife Adebara
Deep Learning and
Natural Language Processing Group
The University of British Columbia
<EMAIL_ADDRESS>
&Muhammad Abdul-Mageed
Deep Learning and
Natural Language Processing Group
The University of British Columbia
<EMAIL_ADDRESS>

###### Abstract

Aligning with ACL 2022 special Theme on “Language Diversity: from Low Resource
to Endangered Languages", we discuss the major linguistic and sociopolitical
challenges facing development of NLP technologies for African languages.
Situating African languages in a typological framework, we discuss how the
particulars of these languages can be harnessed. To facilitate future
research, we also highlight current efforts, communities, venues, datasets,
and tools. Our main objective is to motivate and advocate for an Afrocentric
approach to technology development. With this in mind, we recommend what
technologies to build and how to build, evaluate, and deploy them based on the
needs of local African communities.

##  1 Introduction

Language is the foundation on which communication rests, allowing us to share
ideas and interact with one another. Cultures are built on this foundation. We
cannot understand, nurture, or help a culture thrive without understanding and
nurturing the language carrying it. Language, in turn, is incubated and
evolved by culture Fourie (1995). Each culture is thus naturally best
expressed using the language in which it evolved, which encodes knowledge
about people, their traditions, wisdom, environment, and how they interact
with the sum of the concepts that belong to their own culture. Technology is
an element of culture that arguably both shapes and is shaped by it.
Technology interacts in complex ways with other elements of culture such as
gender, race, and class. Natural language processing (NLP) technologies are no
exception, and play an increasingly important role in today’s world.

Figure 1: African languages discussed in this paper. A high quality version is
in Figure F.1 (Appendix).

Modern NLP technologies, however, have primarily been developed in Western
societies. As such, they often function within contexts of values, norms, and
beliefs that reflect these societies and serve their needs. On the other hand,
the very methods employed to develop most of these technologies and the
knowledge on which they rest also derive from the same Western-Centric
approaches. This poses challenges to the extension and use of these
technologies in communities with different social fabrics that speak different
languages. The scale of this problem is huge, because the majority of the
world’s 7000+ living languages Eberhard et al. (2021) are not NLP-supported.
Apart from perhaps two dozens of popular languages, most languages of the
world are under-resourced, indigenous, and/or endangered. Most African
languages fall within this category and are the focus of this paper (Figure
1).

Our goal is to discuss the major linguistic and sociopolitical challenges
facing development of NLP technologies for African languages.111We do not
cover Arabic since it is spread in both Africa and Asia, and has a sizeable
NLP community. In doing so, we both motivate and advocate for an Afrocentric
approach to technology development where what technologies to build and how to
build, evaluate, and deploy them arise from the needs of local African
communities. We start by typologically situating African languages and
providing illustrating examples as to what makes them challenging from a
computational linguistics perspective (2). Next, we discuss consequences of
the literacy situation in Africa on NLP (3). We then further explain why the
classical binary approach to technology development of feature engineering vs.
end-to-end solutions familiar to most NLP researchers is not ideal for the
African context (4). We follow by data quality (5). To facilitate future work,
we also point to ongoing community efforts, venues, and datasets (6). We
conclude in 7.

##  2 Why Typology Matters

Although it has been argued that the best way to achieve cross-linguistically
useful NLP is to leverage findings of typological research Bender (2016), most
NLP work remains Indo-Eurocentric in terms of algorithms for preprocessing,
training, and evaluation. This is a mismatch to the fact that every NLP
approach requires either explicit or implicit representative linguistic
knowledge O’Horan et al. (2016); Ponti et al. (2019); Bender (2016). Knowledge
of linguistic typology can indeed be very useful for both language-specific
and language-independent NLP O’Horan et al. (2016), including for African
languages. This knowledge can be useful for determining which languages may be
treated together (e.g., in multilingual models) and/or which methods are best
suited for a language-specific task (e.g., a method can be deemed potentially
useful if it has been applied successfully on a language with a similar
typology). To illustrate what typological information can concretely mean for
African languages, it may be useful here to list a number of the most notable
typological features prevalent in African languages across several language
families including Afro-Asiatic, Austronesian, Niger-Congo, Nilo-Saharan,
Indo-European and Creole. These features include use of tone, open syllables,
vowel harmony, splitting verbs, serial verb construction, reduplication, use
of very few or no adjectives (closed class of adjectives Segerer (2008)), and
a large number of ideophones. We will discuss three of these features which we
judge as largely absent from most of the top $10$ NLP-popular languages.222We
use the language diversity index of Joshi et al. (2020) to select the top $10$
languages. We provide a list indicating presence of one or more of these
features in over $100$ African languages in Appendix Table B.1.

###  2.1 Tone

Phonemic tone is characteristic of many African languages, with $\sim 80\%$ of
these languages being tone languages Hyman (2003); Creissels et al. (2008).
This includes most languages of the Niger-Congo family, except Swahili, Wolof,
Serer, Cangin, and Fulani which are not tone languages. All Nilotic and
Khoisan languages and many Afroasiatic languages are also tonal. A smaller
number of languages, including Somali and many Bantu languages, are tonal
accent languages, in which a distinctive or demarcative accent is expressed by
a toneme of high pitch Clements and Rialland (2007).

Tone can occur both at the lexical and grammatical levels. Lexical tones are a
difference in pitch that distinguishes one lexeme from another. In Yorùbá, for
instance, lexical tone is responsible for the differences in meaning in the
following: igbá (calabash, basket), igba (200), ìgbà (time), ìgbá (garden
egg), and igbà (rope). Grammatical tone, on the other hand, distinguishes one
grammatical category from another. In Akan, a language with both lexical and
grammatical tone, grammatical tone distinguishes habitual and stative verbs as
in: Ama dá ha ‘Ama sleeps here’ and Ama dà ha ‘Ama is sleeping here’.
Grammatical tone is also used to indicate case in some Bantu languages
Creissels et al. (2008); König et al. (2008), as a definite marker, for
inflectional or derivational purposes, or to code spatial relations Creissels
et al. (2008).

Two approaches have been adopted in the orthographies of African tone
languages: no tone marking or tone marking. No Tone Marking. Hausa, spoken in
Niger and Nigeria, has grammatical tone but adopts a no tone marking approach
in its orthography. This results in ambiguities that may not be resolved in
context as in jáá tàfí ‘He went’, jáà tàfí ‘He may go’, and jà tàfí ‘He should
go’ Cahill (2019). It is worth mentioning that no tone marking makes little
difference in tone languages with few minimal pairs. NLP systems designed for
a tone language without tone marking may therefore suffer from issues with
ambiguity, if contextual information is not adequate for disambiguation or if
many minimal pairs exist in the language.

Tone Marking. Languages that mark tone may adopt a shallow marking (Yorùbá) or
deep marking approach Cahill (2019); Bird (1999a) by using diacritics,
punctuation marks, or letters to indicate tone Cahill (2019). A shallow
marking approach uses the surface level tone after phonological rules (such as
assimilation) that change the representation of tones have been applied. The
implication of this type of approach is that the same word will have different
tone representations in different contexts. In a low-resource scenario,
therefore, each word will have fewer occurrences and some contexts may not be
seen in training data Bird (1999b) (i.e., data sparsity). For languages that
adopt a deep marking approach, a word would have the same tone,
orthographically, in every context. However, the speech token representing the
same word will vary, thus creating ambiguity at the speech front. Although
adopting a shallow or deep marking approach may not have significant
implications on languages with few tone phonological rules, the degree of
shallow-to-deep marking may increase ambiguity for languages with many
phonological rules Bird (1999b, a). Tone-marking can also be partial or
exhaustive. Partial Tone-marking. Some African languages such as Yorùbá adopt
a partial tone marking approach with diacritics. Yorùbá has three distinctive
tones - high, mid and low tones - but only represents the high tone with the
acute symbol and the low tone with the grave symbol in its orthography. The
mid tone is not marked and vowels without diacritics unambiguously indicate
the presence of the mid tone. Rangi, a language spoken in Tanzania, marks only
high tone on nouns while Akoose, a language spoken in Cameroon, marks high
tone and contour tones but leaves low tones unmarked Cahill (2019). Karaboro,
spoken in Burkina faso, marks grammatical tones in plurals using a word final
hyphen as in: sààpjé ‘Rabbit’ and sàápjé- ‘Rabbits’. Exhaustive Tone-marking.
In exhaustive tone-marking, every tone bearing unit is orthographically marked
for tone as in Dschang, spoken in Cameroon Bird (1999b).

Furthermore, a higher number of distinctive tones increases ambiguity. In Dan,
a language with five distinctive tones, the following can occur: gba1
(caterpillar), gba2 (shelter), gba3 (fine), gba4 (roof), and gba5 (antelope)
Clements and Rialland (2008). For another example, Yorùbá has three
distinctive tones where each monosyllabic sequence of sounds can have up to
three pitch contrasts and a bi-syllabic can have $2^{3}$ pitch
contrasts.333However, some phonological rules can restrict the occurrence of
certain combinations and there may be lexical gaps. For instance, the high
tone occurs only in marked consonant-initial words.

Recommendations. (1) For speech applications, there exists a plethora of
unexplored research questions to answer with regard to the implication of tone
on text-to-speech and text-free speech processing Lakhotia et al. (2021). We
therefore call for empirical studies that investigate the influence of tones
in text-to-speech, text-free speech processing, and universal speech
processing Yang et al. (2021). Since tone is absent in Indo-European languages
where most recent speech work is situated, we expect this to be a fruitful
direction. (2) For text applications, tone will be relevant for natural
language understanding (NLU) tasks including but not limited to part of speech
tagging (POS), text classification, and natural language generation (NLG)
tasks such as machine translation. For many of these applications, it is not
clear how tone would interact with system performance. For example, we do not
know where to keep and where to remove tone (if at all). For example, we find
that while removing tone has negligible impact on Bambara$\rightarrow$English
MT, it has significant negative impact on Yorùbá$\rightarrow$English (see
Table A.2 in Appendix). We also do not necessarily know what the best ways to
encode (and decode) tone information are. (3) For work involving languages
with shallow tone marking at the orthographic level, we recommend budgeting
for collection and preparation (e.g., annotation) for sizeable datasets (to
alleviate data sparsity). In absence of large datasets, knowledge of the
finite phonological rules of a language can also be exploited for generating
data for downstream tasks. (4) Orthographic conventions should not be taken as
a good indication of the functional load (i.e., information load) of tone in a
language, for there are many non-linguistic (e.g., political) reasons for
employing a particular orthographic convention Cahill (2011). Hence, NLP
researchers should do due diligence as to understanding how tone works in a
given language. (5) Punctuation marks may be tone indicators, and care needs
to be taken on how these are pre-processed.

###  2.2 Vowel Harmony

Vowel harmony is a phonological pattern in which vowels within a given domain
agree in properties such as tongue position or lip rounding Hyman (2003). It
restricts the possibilities of vowels that can co-occur Archangeli and
Pulleyblank (2007). Different languages adopt different types of vowel
harmony. Three types of vowel harmony that are unique to African languages
have been recorded in the literature Clements and Rialland (2007): (i)
advanced tongue root (ATR) harmony, (ii) cross height ATR harmony, and (iii)
reduced ATR harmony. ATR harmony occurs when some vowels have the $[-ATR]$
feature and others have the $[+ATR]$ feature. Within a word, all non-low
vowels agree in $[+ATR]$ or $[-ATR]$ features. With cross height ATR, $[+ATR]$
in mid vowels require $[+ATR]$ in high vowels and vice versa. The reduced ATR,
on the other hand, occurs in languages with only one mid vowel and $[-ATR]$
mid and high vowels shift to $[+ATR]$ in the context of $[+ATR]$ high vowels
Clements and Rialland (2007).

Recommendations. (1) Since vowel harmony is largely absent in most Indo-
European languages, knowledge of vowel harmony is currently underexplored in
NLP. Such a knowledge can be useful for tasks such as POS tagging since tokens
with the same part of speech tend to have similar harmonies. (2) Automatic
spelling checkers can also exploit information about vowel harmony since
certain co-occurrences of vowels are barred by phonological rules of vowel
harmony.

###  2.3 Serial Verb Constructions

Serial verb constructions (SVC) involve two or more verbs that combine as a
whole without any indication of dependency or any conjunction between them
Creissels et al. (2008); Déchaine (2008). Languages with SVC use serial verbs
to encode events that are usually encoded as single verbs in Indo-European
languages. This poses a unique problem when creating/evaluating cross-lingual
embeddings and in applications such as dictionary creation. For instance
translating from English to Yorùbá, we have the following examples: borrow \-
‘Gbà àwìn (receive credit)’, believe \- ‘Gbà gbó (receive hear)’, pinch \- ‘Já
l’ éèékáná (cut with fingernails)’ so that a single English verb is a serial
verb in Yorùbá. When these words are used in sentences, they may have
intervening words as in: Gbà á l’ áawìn (receive 3SG-O on credit) ‘borrow it’,
Gbà á gbó (receive 3SG-O hear) ‘believe it’, Já a l’ éèékáná (cut 3SG-O with
fingernails) ‘pinch it’. In Africa, serial verb constructions are very common
in Kwa (e.g. Ewe) and Western Benue-Congo languages (e.g. Yorùbá). They have
also been recognized in the North Khoisan language !Xun.

Recommendations. (1) Given how pervasive word embedding models are in most NLP
applications, we recommend investigating how embeddings accounting for SVC can
be developed. Similarly, SVC will have bearings in how (cross-lingual)
embeddings are evaluated. For example, researchers may need to create
dictionaries customized to African languages. (2) For POS tagging, decisions
need to be made on what approach to take in treating such constructions. (3)
Research investigating the extent to which SVC affects performance across
different tasks needs to be explored. For example, this can be valuable for
parsing and MT.

##  3 No Literacy, No NLP

NLP for high resource languages (HRL) benefits from the level of literacy NLP
researchers have in these languages. Most researchers usually have literacy
beyond high school in one or more of the languages they work on. In Africa,
however, with very complex multilingual societies, many educated Africans
cannot read nor write their Indigenous languages.444We use Indigenous
languages to refer to languages native to Africa. These people do not have
basic linguistic knowledge in their languages either. For example, many people
do not know which words are nouns or verbs Cahill (2001). For context, more
than $2,000$ languages have been reported in Africa - about $1/3$ of all the
languages in the world Hammarström (2018) \- making many African communities
truly multilingual. As a result, it is not uncommon for a child to be exposed
to multiple Indigenous languages before reaching school age. This is
especially the case in families where the father, mother, and grandparents all
speak different languages (which may, in turn, be different from the languages
spoken in the communities they live in). People who receive formal education -
the sole way people become literate - thus attain only partial literacy in one
or more African language(s) which may not even be their mother tongues. Many
others have no knowledge of any Indigenous language, and are only literate in
a foreign language Cahill (2001); Ouane and Glanz (2010).

As seen in Table C.1 in the Appendix, out of the $56$ countries in Africa,
only $17$ countries have an Indigenous language as a national language
(although in $14$ of these $17$ countries, a foreign language is the main
official language). Furthermore, the countries that give any official status
to Indigenous languages, tend to restrict such a status to those languages
belonging to majority speakers.555It is worth mentioning that some of the
excluded languages have millions of L1 speakers. For example, in Nigeria, only
three out of $512$ languages are officially recognized as regional languages;
Ghana uses $10$ of its $73$ Indigenous languages as institutional languages;
Swahili is the only official Indigenous language in Tanzania out of $118$
others; $12$ of $61$ languages in Kenya have some official status; only $12$
of $20$ Indigenous languages in South Africa are institutional languages. This
challenging situation is the result of poor language policies, which we now
turn to.

Language policy. Language policy determines which languages are used in
education, media, commerce, and almost every domain controlled by government.
With most Africans educated in English, French, Portuguese or majority African
languages, most African languages (those without any official status) are
rarely used or used only at home Petzell (2012); Foster (2021); Ouane and
Glanz (2010). In countries where an Indigenous language has official status,
governments and implementing bodies only pay lip service to these policies
Kaschula and Kretzer (2019). In addition, lack of trained personnel and
adequate educational resources in Indigenous languages, as well as rarity of
teachers sufficiently proficient to offer Indigenous language courses, make
policies difficult to implement Trudell (2018); Kaschula and Kretzer (2019).
Furthermore, in many schools, Indigenous languages are referred to as
vernaculars and are prohibited. Violation usually attracts fines, and even
corporal punishment in some cases. English and other foreign languages remain
the prerequisite for scientific and technological development, and a key to
social prestige and power. Students who do not pass examinations in these
foreign languages cannot continue studying beyond elementary school Foster
(2021); Petzell (2012); Mohr (2018). Effect of these currently implemented
policies is visible in the NLP situation of African languages. Languages
officially recognized within their countries have more resources and tools for
NLP than those that do not. For instance, all African languages with a
diversity index Joshi et al. (2020) greater than zero are either official
national, regional, or educational languages or are languages of wider
communication Eberhard et al. (2021). We provide more details about available
resources of different types (labelled, unlabelled, parallel, and raw) and
tools in Section F (Appendix).

Recommendations. Partial and lack of literacy or knowledge of Indigenous
languages has significant negative impacts on NLP in African languages.
Therefore, (1) we include in our concept of a grand challenge the development
of language policies that facilitate literacy in Indigenous African languages.
Literacy improvement takes time, and policies that teach Indigenous languages
only for brief periods in elementary school need to be reformed. (2) We also
recommend the implementation of policies that require use of Indigenous
languages in media, government, and other domains. (3) Adequate funding needs
to be allocated to develop pedagogical materials, train teachers, and provide
teaching aids in order to facilitate the implementation of these policies.
Simply put, without improvement of literacy in African languages, we do not
see a flourishing future for African NLP.

##  4 A Tale of Two Approaches

There are two main approaches for developing NLP systems. We discuss each of
these vis-a-vis the situation for African languages here, giving relevant
recommendations.

Feature engineering. Feature engineering requires domain knowledge, which is
lacking for many African languages due to the aforementioned literacy
situation. This negatively impacts use of written African languages in many
domains of human endeavor, let alone NLP research. Weak literacy simply means
unavailability and inaccessibility of linguists, annotators, language experts,
and computational linguists with expertise in African languages. It also
manifests itself in lack of grammars, primers, teaching aids, and dictionaries
Cahill (2011). As it turns out, grammatical information is either fully
lacking or under-documented for almost half of Africa’s languages. This makes
Africa the second least known continent (after Oceania, dominated by the New
Guinea area) Güldemann (2018). In Appendix Table F.2, we list available
linguistic resources for all African languages we could trace.

Deep Learning Approaches. A major bottleneck in the development of end-to-end
deep learning NLP systems for African languages is the paucity of machine-
readable data Adda et al. (2016). Deep learning systems for high-resource
languages are usually fed ever-growing amounts of data that are abundant
online and via several other avenues in today’s connected society. Without
these type of (interactive) data, it is challenging to develop NLP models for
real-world use. In particular, models that are endowed with the implicit and
explicit knowledge embedded in language are hard to build (at least by current
technologies) without large volumes of data derived from diverse contexts.
Many African languages lack the environment from which these types of machine-
readable data can be collected. Social media, which is a venue for data
collection for many high-resource languages, are often not widely used for
African languages. In fact, most Africans post to social media in foreign
languages rather than in Indigenous African languages Malatji
(2019).666https://www.talkwalker.com/quick-search. One reason behind this
issue is unavailability of keyboards for Indigenous languages. Most keyboards,
for example, do not support symbols for representing tone and some other
grammatical features. Partial or complete lack of writing literacy is another
reason. A third reason is related to the lack access to smart machines and
internet connectivity.

Furthermore, countries such as Nigeria where official status is given to a
handful of Indigenous languages, still document official activities in foreign
language exclusively. Media organizations that often read the news in a
foreign language as well as local languages also archive only the English news
and discard those in Indigenous languages. All such practices stifle
opportunities for developing large datasets for African languages, effectively
causing African NLP to lag behind. If archived, data for many Indigenous
African languages can facilitate development across a wide host of speech and
language tasks, including text-to-speech and machine translation.
Collectively, these compounded issues mean there are only few (and often
smaller) online communities that contribute to web fora, Wikipedias, and other
platforms where data are growing in large-to-massive amounts for high-resource
languages. This is evident in the diversity index for African languages
offered by Joshi et al. (2020).

According to Joshi et al. (2020) who summarized the digital status and
‘richness’ of languages in the context of data availability, $542$ African
languages are left-behinds. That is, these languages have exceptionally
limited resources that will make it probably impossible to lift them up in the
digital space. A total of $26$ African languages are scraping-bys and are in a
better position than the left-behinds. However, even these are said to require
organized awareness and strong data collection effort with most of these
languages having no labelled datasets. Only nine African languages are in the
hopefuls category, with a small set of labeled datasets, researchers, and
language support communities. A single African language (i.e., Afrikaans) is
in the rising-stars category with a strong web presence and a thriving
cultural community online (although with insufficient efforts in labeled data
collection). We offer a summary of the diversity index for $578$ African
languages in Table F.6 in the Appendix.

Recommendations. (1) We recommend that daily engagements in education,
commerce, media, and government which are otherwise archived only in foreign
languages (see Table C.1), be archived in Indigenous languages as well. These
would comprise valuable sources of labelled and unlabelled machine-readable
data for NLP, let alone painting a more equitable and representative picture
of African languages. (2) Humans and machines complement each other’s
strengths, so we recommend stronger interactions between NLP experts and
theoretical linguists or knowledgeable native speakers when developing
resources and models for African languages. (3) Funding should also be
allocated to theoretical linguists and language experts, along with machine
learning and NLP experts, to aid this work. (3) For African languages with
available linguistic research, it has been found that certain POS,
morphological, named entity, and dependency information can be accurately
retrieved automatically by using tone, vowel harmony, or even syllable
structure patterns Adegbola (2016). These approaches may aid faster
development of POS taggers, lemmatizers, NER, or even dependency parsers. (4)
When developing NLP pipelines for African languages, removal of numbers and
non-alphanumeric symbols should be approached with caution. This should
especially be the case for languages with insufficient research as to the
functions played by these symbols, and would help avoid making any
irrecoverable issues in the data. (5) The most effective ways for building
pipelines for African languages remains an under-explored area of research. We
therefore call for empirical studies that investigate development of viable
pipelines. (6) We emphasize the need to respect user consent, data
sovereignty, wishes of local communities, and other important issues such as
privacy while carrying out any collection or archival effort Sutherland
(2018); Daigle (2021); Makulilo (2012). This is to prevent the predatory use
of data collected from local communities including monitoring or controlling
local peoples, censorship, and other surveillance activities. Properly
handling data mitigates physical, financial, and other security risks that
poor data practices expose local communities to Turianskyi (2018) and must
also be prioritized. We now further discuss issues around data quality.

##  5 Garbage in, Garbage out

A manual evaluation of $205$ datasets involving African languages such as
those in CCAligned El-Kishky et al. (2020), ParaCrawl Bañón et al. (2020);
Esplà-Gomis et al. (2019), WikiMatrix Schwenk et al. (2021), OSCAR Ortiz
Suárez et al. (2020), and mC4 Xue et al. (2021) show that at least $15$
corpora were completely erroneous, a significant fraction contained less than
$50\%$ of correct data, and $82$ corpora were mislabelled or used ambiguous
language codes Kreutzer et al. (2021). The inaccuracy is due to a lack, or
poor quality of language identification tools, dictionaries, and text pre-
processing piplelines, for many low resource languages including African
languages represented in these datasets. Furthermore, available resources are
rarely evaluated especially when crawled as part of a multilingual dataset.
Furthermore, Alabi et al. (2020) find that, fastText embeddings for Yorùbá has
an estimated 135K out of 150K words belonging to other languages such as
English, French, and Arabic. New embedding models created by Alabi et al.
(2020) with a curated high quality dataset outperform the off-the-shelf
fastText embeddings even though the curated set has fewer words. Results of
these few studies paint a gloomy picture for most current multilingual
datasets involving African languages, and models derived from them.

Inconsistent orthographies also contribute to the data quality problem
Martinus and Abbott (2019). In many cases, orthographies may not be
standardized and will have significant spelling and punctuation variations
across different domains. In some cases where standard orthographies exist,
word lists or dictionaries do not necessarily represent the standardized
orthography. Using Hausa as an example, all commercially published books and
nearly all Hausa language newspapers use the standard romanized orthography.
Standard romanized orthography is written without tones or any indication of
vowel length Schuh and Yalwa (1993). The orthography used in grammars,
dictionaries, and pedagogical documents on the other hand, indicate tone and
vowel length Schuh and Yalwa (1993). Furthermore, languages that have standard
orthographies may also suffer from inconsistencies when orthographic
conventions are not adhered to Olúmúyìwá (2013). This is evident in the
methods and practices for content archiving of many African languages on the
web. For example, all VOA websites, omit tones for African languages whose
standard orthographies require tone diacritics. BBC also does not adhere to
the orthographic conventions for Yorùbá texts except in the headlines, JW.org
also does the same for some African languages.

Apart from the aforementioned issues, lack of constant and systematic use of
African languages in contexts such as governance, law, technology, science,
and education prevents African languages from expanding in vocabulary to
accommodate new concepts that have become important parts of conversation
elsewhere. As a result, it is not uncommon to have large amounts of foreign
words in a dataset which are not adapted to the phonological or orthographic
structure of the target African language. Furthermore, terminologies continue
to be employed inconsistently and spelt differently in many African venues.

To provide a concrete example of the data quality problem for African
languages, we perform a manual evaluation of Flores-101 dataset Goyal et al.
(2021); Guzmán et al. (2019b) for Yorùbá. We find the following: (1) $5.29\%$
spelling errors (2) $2.7\%$ inconsistent spellings (3) $1.2\%$ borrowed words
not adapted to the orthographic conventions of target language and (4)
$12.4\%$ incorrect tone marks. Detailed information is in Appendix G.

It is important to mention that a single error in assignment of diacritics,
for instance, can result in significant semantic and syntactic differences in
texts. The implication of inconsistencies in orthography is hence enormous for
low resource African languages. Such inconsistencies worsen the issue of data
sparsity: when different spellings of the same word are employed, or when tone
or other grammatical features are inconsistently marked, the same ‘word’ will
have many more surface forms than what it actually should. Data sparsity can
in turn aggravate the situation for any work involving training with data from
different domains (e.g., in domain adaptation). That is, reliability of models
trained with erroneous data from a source domain will be diminished while
transferring into a target domain. Orthographic inconsistencies also affect
results of search engines Choroś (2005) in that these engines would not
recognize the relationship between a diacritized text and its undiacritized
counterparts Asubiaro (2014); Olúmúyìwá (2013). Again, this results in
difficulty retrieving resources for many African languages. To optimize search
for African languages that involve diacritics, some users employ normalized
text which in turn further creates a mismatch between web documents and other
standard offline documents (e.g., books) for many African languages.

Recommendations. (1) We recommend developing language identification tools
that cover African languages. (2) Development of dictionaries or even extended
word lists will also help the community ensure data quality. (3) Manual
inspection of sizeable samples of multilingual datasets should also continue
to be prioritized. (4) We also suggest orchestrated efforts to enforce
consistency in orthography for the various languages. (5) Linguistic rules may
be appropriate for developing automatic data cleaning and pre-processing, but
development of any such rules should be carried out carefully. We now briefly
highlight community efforts invested in developing skills, datasets, and tools
in the African NLP space.

##  6 Communities and Resources

The majority of existing resources for NLP are the initiative of various non-
governmental organizations determined to develop datasets and tools for
African languages. We list some of these efforts for NLP, but also within the
larger contexts of artificial intelligence. We focus on communities and venues
here and list recent funding initiatives in Table D.1 (Appendix).

Workshops. As far as we know, there are two main venues in the form of
workshops supporting NLP for African languages, and African AI. These are
AfricanNLP and BlackInAI. We provide details about these venues in Appendix E.

Communities. Masakhane, Black in AI, Deep Learning Indaba, Knowledge 4 All
Foundation Ltd (K4A), Zindi and ALTI are some of the active communities for
research on NLP for African languages. More information about each of these
communities is in Section D.

Resources. The religious domain is currently the major source of data for a
large number of African languages. Top amongst religious resources is the
Bible corpus (available in over $1,000$ languages of Africa Resnik et al.
(1999); McCarthy et al. (2020a)) and the JW300 website (with data for $\sim
100$ low-resource African languages). Religious sources are constantly updated
with new data from the same languages and new languages are often added,
making these sources increasingly useful. One issue of these datasets is that,
although they are parallel, they may not be sentence aligned. Regardless,
these resources remain significantly inadequate. Most other data available for
African languages are raw and unlabelled. Still, these can be useful in many
applications (e.g., in training word embeddings or language models, for
backtranslation). We provide more details about available resources (labelled,
unlabelled, and raw) and tools in Appendix F.

Recommendations. (1) To achieve Afrocentric NLP, we recommend active
interactions between differently existing communities, as well as encouraging
new regional and thematically-defined communities. (2) We recommend extending
these communities beyond AI, NLP, and machine learning to involve theoretical
linguists, anthropologists, sociologists, field workers, and other scholars
and practitioners with interest in African languages. (3) We believe ACL and
other similar organizations should continue to prioritize work on low-resource
languages by securing dedicated tracks in their publication and dissemination
venues.

##  7 Discussion and Conclusion

We discussed major challenges facing development of NLP technologies for
African languages. One of the most important recommendations we would like to
emphasize is to prioritize African NLP work based on the needs of African
communities. For example, we believe development for data and tools for
improving health and education should be a priority. We also caution against
extractive practices, and encourage creation of opportunities, contexts, and
venues for work on African languages and advocacy for reclaiming African
language policies. In addition, data literacy and issues around data
sovereignty and privacy should remain of highest importance. We highlighted
various communities and venues here that we think should continue to be
supported.

## Acknowledgements

We gratefully acknowledge support from the Natural Sciences and ENgineering
Research Council of Canada (NSERC; RGPIN-2018-04267), the Social Sciences and
Humanities Research COuncil of Canada (SSHRC; 435-2018-0576; 895-2020-1004;
895-2021-1008), Canadian Foundation of Innovation (CFI; 3771), Compute Canada
(CC),777https://www.computecanada.cacomputecanada and UBC ARC-
Sockeye.888https://arc.ubc.ca/ubc-arc-sockeye Any opinions, conclusions or
recommendations expressed in this material are those of the author(s) and do
not necessarily reflect the views of NSERC, SSHRC, CFI, CC or UBC ARC-Sockeye.
We thank Rose-Marie Déchaine for helpful discussions.

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##  Appendix A Effect of Tone in MT

In this experiment on tone, we used the bible for the Yor-En pairs Adebara et
al. (2021), and LDC dataset (Bamanankan Lexicon LDC2016L01.) for the Bam-Eng
pairs. Details of the data sizes are available in Table A.1.

Pair | Lang | Sent | Words
---|---|---|---
Bam-Eng | Bam | $11,154$ |  $43,786$M
Eng | $11,154$ | $64,571$
Yor-Eng | Yor | $31,086$ | $942,663$
Eng | $31,086$ | $822,950$
Table A.1: Number of sentences and words for the training data used for each
language pair.

We developed python scripts to remove diacritics from Bambara and Yorùbá no-
tone marked settings. In Table A.2, tone significantly affects bleu scores for
En-Yor pairs but has marginal effect in the Bam-En pairs. The influence of
tones thus needs to be further investigated.

Pair | Tone-Marked | No-Tone Mark
---|---|---
BAM-ENG | $1.61$ | $1.61$
ENG-BAM | $1.07$ | $1.34$
ENG-YOR | $32.95$ | $11.51$
YOR-ENG | $38.57$ | $12.76$
Table A.2: BLEU scores for tone-marked and no tone mark settings.

##  Appendix B Language Typology Information

In Table B.1, we provide typology information covering tone, vowel harmony and
SVC for $116$ African languages. The checkmarks indicate the presence of the
specified feature in the language. To the best of our ability, this
information represents the features in the specified languages and for the
specified features. Although we do not claim that this information is
complete. This table was created by perusing grammatical descriptions,
pedagogical materials, and linguistic research regarding these features in the
specified languages.

Language Code Tone T.Marked VH SVC Language Code Tone T.Marked VH SVC Afar aar
Amharic amh Amazigh kab Coptic cop Ge’ez gez Oromo gaz Hausa hau Somali som
Tachelhit shi Tamazight tzm Tamajaq ttq Tamajaq ttq Wolaytta wal Tumbuka tum
Afro-Asiatic Arabic ara Arabic Sudanese Spoken apd Tigré tig Tigrinya tir A.
Malagasy plt Akoose bss Akan aka Akoose bss Bambara bam Bassa bsq Bemba bem
Chitonga toi Chichewa nya Dagaare dga Dagbani dag Ewondo ewo Farefare gur Fang
fan Efik efi Éwé ewe Edo bin Esan ish Dangme ada Fulah ful Fulfulde fuv Limba
lma Fulfulde fuv Ga gaa Igala igl Kabiyè kbp Kpelle xpe Kikuyu kik Kinyarwanda
kin Igbo ibo Mbukushu mhw Mampruli maw Ndonga ndo Medumba byv Mende men Lunda
lun Ndebele nbl Jula dyu Kamba kam Kabiyè kbp Isoko iso Kaonde kqn Karaboro
kza Kimbudu kmb Fante aka Kwanyama kua Luganda lug Kongo kwy Kwangali kwn
Niger Congo Twi aka Chitumbuka kwn Tswa tsc Tshiluba lua Tsonga tso Zama xuu
Limba lma Lukpa dop Pular fuf Kissi kqs Rundi run Setswana tsn Shona sna
Swahili Congo swc Swati ssw Swahili swh Swahili swa Sepedi nso Sesotho sot
Tsonga tso Themne tem Comorian Ngazidja zdj Urhobo urh Tshiluba lua Venda ven
Wolof wol Xhosa xho Lingala lin Yemba ybb Yorùbá yor Zande zne Mboshi mdw Zulu
zul Tiv tiv Kanuri knc Dinka dik N.S. Kunama kun Bari bfa Luo luo Dendi ddn
Afrikaans afr English eng French fra Portuguese por I.E. Spanish spa Urdu urd
Kituba ktu Juba Arabic pga Creole Seychelles French Creole crs Sango sag
Nigerian Pidgin pcm Kabyverdianu kea

Table B.1: List of Languages, language codes and typology of the languages
presented in this paper across 6 language families: Austronesian (A.), Nilo-
Saharan (N.S.), and Indo-European (I.E.). The checkmarks are added to each
language to indicate the presence of the corresponding feature.

##  Appendix C The Language Situation in Africa

In Table C.1 we list the status for different languages in Africa. This table
was created using information available on ethnologue Eberhard et al. (2021)
for each African country. The national, regional, educational and Indigenous
languages are presented as it applies to each country. We present all African
countries including those not officially recognized in this list. To the best
of our knowledge, this list is a true representation of the status of
languages used in Africa.

All African countries, except Ethiopia and Liberia were colonized, with most
gaining independence between the 1950s and the 1970s. The colonialist came
from different parts of Europe and adopted different language policies which
seem to play an important role in the language policies adopted by different
African countries today. Although economics, politics, and globalization also
play a crucial role. All colonialists interacted derogatorily with Indigenous
languages and often referred to them as vernaculars. Although, the British
colonialists allowed Indigenous languages in their territories if desired. The
French, Spanish, and Portuguese on the other hand did not tolerate any
Indigenous languages in public. Despite the British’s tolerance for Indigenous
languages, Indigenous languages were allowed only in early childhood education
and Indigenous languages where prohibited after the 4th year in elementary
school Williams (2013); Ouane and Glanz (2010).

From Table C.1, it is evident that colonial languages have retained their
official status in many African countries till date Khejeri (2014). Foreign
languages are dominantly used in education, and most official government
functions, even in countries where Indigenous languages have official status
Banda (2009). According to Ouane and Glanz (2010), only $25\%$ of the
languages used in secondary education and $5\%$ of the languages in higher
education are African. This is despite the known benefits of using Indigenous
languages in Education and minority language development Bühmann and Trudell
(2008); Trudell (2005); Williams (2013); Bull (1955). In cases where policy
favours the official use of Indigenous languages, some governments have shown
a lack of political will to implement these policies Williams (2011). The
current linguistic situation thus seem to be one of convenience rather than
one from well developed language policies.

Despite a few dissenting voices who argue that the use of several mother
tongues will accentuate inter-tribal conflict Khejeri (2014), the general
consensus is that preserving language diversity through policies that
encourage multilingualism are most desirable. Developing a truly multilingual
language policy for Africa will certainly be challenging Ouane and Glanz
(2010), but will be most beneficial even to the progress of NLP on the African
continent.

Region Country Lang(s) Ind. National Regional Educational Burundi 4 2 run
Comoros 7 2 fra, ara zdj Djibouti 5 2 fra, ara Eritrea 15 9 ara kun, tig
Ethiopia 91 87 amh aar, gaz, som, tir 31 Ind. and 1 foreign Kenya 68 61 eng,
swa 11 Ind. Madagascar 14 12 fra, mlg (higher ed.) Malawi 17 13 eng tum
Mauritius 9 2 eng, fra urd Mayotte 4 2 fra Mozambique 44 42 por Reunion 3 1
fra Rwanda 4 2 eng, fra, kin Seychelles 3 1 eng, fra, crs Somalia 13 10 ara,
som eng East Africa South Sudan 70 59 eng pga, zne, apd, bfa 8 Ind. Tanzania
126 118 swa eng and swa Uganda 44 41 eng, swa 2 Ind. , 1 non-Ind.. Zambia 46
37 eng 3 Ind. 4 Ind., 1 non-Ind. Zimbabwe 22 16 eng 2 Ind, 2 South African
Angola 46 41 por Cameroon 275 271 eng, fra Central Afr. Rep. 75 65 eng, sag
Chad 129 123 fra, ara Congo 66 55 fra Dem. Rep. of Congo 214 209 fra 2 Ind.
Equatorial Guinea 15 12 spa 2 foreign Gabon 43 40 fra Middle Africa Sao Tome e
Principe 7 3 por 1 foreign Algeria 19 14 ara, kab 1 foreign Egypt 19 9 ara
Libya 9 8 ara Morocco 15 10 ara, tzm Sudan 75 70 ara, eng North Africa Western
Sahara 4 2 ara Botswana 31 26 eng and tsn 1 foreign Eswatini 5 1 eng, ssw
Lesotho 5 3 eng and sot Namibia 28 23 eng 3 foreign, 6 Ind. South Africa South
Africa eng, afr, nbl, tsn, nso, sot, ssw, 31 20 tso, ven, xho, zul 1 foreign
Benin 55 50 fra Burkina Faso 71 66 fra Cape Verde Islands 2 1 kea, por Cote
d’ivoire 87 75 fra 1 foreign Gambia 11 7 eng Ghana 83 73 eng gur, maw 5 Ind.
Guinea 37 35 fra fuf 3 Ind. Guinea-Bissau 23 18 por Liberia 31 27 eng Mali 68
63 fra 5 Ind., 1 foreign Mauritania 7 5 ara Niger 23 19 fra 2 Ind., 1 foreign
Nigeria 522 512 eng hau, ibo, yor 5 Ind. Saint Helena 1 0 eng West Africa
Senegal 39 31 fra Sierra Leone 24 19 eng lma, men, tem Togo 44 40 fra

Table C.1: A statistics of the language use in Africa computed from Eberhard
et al. (2021). For each country, we show the number of languages (lang)
reported, the number of Indigenous languages spoken in the country (Ind.), the
national languages, the regional languages, and the educational languages.

##  Appendix D Communities

Many communities contribute significantly to the development of NLP for
African languages. We list some of them below. Masakhane aims to build an
active community geared at creating resources that are truly representative of
African culture, facilitating collaborations to develop African NLP and
lowering the barriers for NLP participation. They achieve this by having an
active slack channel that fosters interaction between stakeholders, organizing
workshops, creating easy to use google colab notebooks among several other
initiatives. Masakhane so far has over 1,000 members.

Black in AI is an organization that focuses on increasing the presence,
inclusion, and visibility of black people in artificial intelligence. They
achieve this objective through advocacy, mentorship, and facilitating
collaborations. Although BlackinAI encompasses black people beyond the African
continent, and they do not specifically restrict their operations to African
languages, it is a great community for collaborations.

Deep Learning Indaba is an organisation whose mission is to strengthen machine
learning and artificial intelligence in Africa by enabling Africans to be
active shapers and owners of AI technologies. Deep Learning Indaba which was
inaugurated in 2017 organizes an annual Deep Learning Indaba retreat for
teachings and practical sessions on AI. They also provide mentorship programs
and grants (the IndabaX) that fund AI gatherings in 26 African countries with
plans underway to include more countries. This is in addition to awards for
the application of AI to an African problem, for excellence in research in
tertiary African institutions, and for services to the machine learning
community in Africa- Kambule, Maathai, and Umuntu awards respectively. These
programmes aim to build a sustainable pan-African community of AI expertise,
create local leadership in AI in every country across the continent, and
recognise excellence in research and application of AI technologies,
respectively.

Knowledge 4 All Foundation Ltd (K4A) pioneers machine learning methods of
pattern analysis, statistical modeling, and computational learning and
transforms these into technologies for large scale applications in open
education. They organize symposiums, summer schools, workshops, colloquiums,
and conferences. They also provide fellowship to develop datasets and
strengthen capacities and innovation potential for low resource African
languages under the international development program. They have developed
resources for Ewe, Fongbe, Yorùbá, Chichewa, Wolof, Kiswahili, Tunisian
Arabizi, Twi, and Luganda. They also various competitions to develop or
improve methods for NLP of African languages.

Zindi hosts a large community of African data scientists and facilitates
collaborations between data scientists and organizations. They provide a place
to learn, improve skills and find a job. They also organize competitions for
data collection tasks and developing NLP models for various African languages.

ALTI is one of the pioneering NLP communities in Africa. They focus on making
computers usable in African languages and develop and grow human talent that
take African Languages into the information age. They also provide a hub were
NLP enthusiasts can be mentored for NLP work in African languages.

Different organization provide funding for NLP research. Some of these
organizations are presented in Table D.1.

Organization | Type
---|---
Google | Industry
Microsoft | Industry
The Rockefeller Foundation | Foundation
FAIR Forward | Government
Lacuna Funds | NGO
Knowledge 4 All | Research
IDRC | Research
Table D.1: Some funding Organization for African NLP including Non-
Governmental organizations (NGO)s

##  Appendix E Workshops

The AfricanNLP workshop has run annually alongside ICLR and EACL in $2020$ and
$2021$ respectively. In $2020$, $32$ papers were presented while in $2021$,
$40$ papers describing different systems were accepted. Currently, papers
submitted are non-archival, giving authors the opportunity to submit the
papers to other venues. BlackInAI has organized yearly workshops co-located
with Neural Information Processing Systems (NeurIPS) since $2017$. Audience is
composed of researchers who self-identify as Black and often has many works
related to African languages.

##  Appendix F Resources

###  F.1 Labelled Resources

Majority of labelled corpora is developed as part of the development process
of many NLP tasks. This is due to a lack of readily available labelled corpora
for many NLP tasks. Labelled corpora has been developed for MT Adelani et al.
(2021a); Nekoto et al. (2020); Tapo et al. (2020); Emezue and Dossou (2020);
Ezeani et al. (2020); Hadgu et al. (2020), classification Niyongabo et al.
(2020); Fourati et al. (2020); Oyewusi et al. (2020), automatic spelling
correction Gezmu et al. (2018), morphological segmentation Outahajala and
Rosso (2016); Mott et al. (2020), NER Adelani et al. (2021b); Hedderich et al.
(2021), diacritic restoration Orife et al. (2020a); Asahiah et al. (2017),
automatic speech recognition (ASR) Dossou and Emezue (2021); Tachbelie et al.
(2020), and speech translation Godard et al. (2018). A summary of labelled
corpora can be found in Table F.5.

A few of the labelled corpora are developed by trained linguists and language
experts Strassel and Tracey (2016a); Adebara et al. (2021) while others are
collected by native speakers Adelani et al. (2021b, a); Nekoto et al. (2020).
Furthermore, evaluation is often done using automatic metrics that measure
model performance rather than data quality or inter-annotator agreement
Outahajala and Rosso (2016). Data is also often labelled on the assumption
that the data has been proofread Gezmu et al. (2018), while the procedure for
developing the dataset is often not discussed. It is important to mention here
that we advocate that trained linguists or language experts, particularly
those trained in African languages, be involved in data collection or curation
activities for African languages. This is because of the linguistic situation
in Africa and the literacy levels in African languages which have been
discussed in this paper.

###  F.2 Unlabelled Corpora

Unlabelled corpora seem to be the bulk of available data for African
languages. Most corpora are crawled from the web as part of multilingual
corpora development efforts like JW300 Agić and Vulić (2019), ParaCrawl Bañón
et al. (2020); Esplà-Gomis et al. (2019), WikiMatrix Schwenk et al. (2021),
OSCAR Ortiz Suárez et al. (2020), mC4 Xue et al. (2021), CCAligned El-Kishky
et al. (2020), wikiAnn Pan et al. (2017). We provide a summary of unlabelled
corpora in Table F.5.

###  F.3 Crosslingual Tools

Pre-trained models like BERT Devlin et al. (2019), ELMo Peters et al. (2018),
Roberta Liu et al. (2019), GPT Radford et al. (2018, 2019); Brown et al.
(2020), BART Lewis et al. (2020) have advanced the state of the art in a wide
variety of tasks, suggesting that these models acquire valuable, generalizable
linguistic information during the pre-training process. However, training
language-specific models is possible for only a few languages which have large
amounts of data. A popular alternative has been multilingual language models
(MLM) such as mBERT Devlin et al. (2019), XML-R Conneau et al. (2020), MT5 Xue
et al. (2021), mBART Liu et al. (2020) and many others. MLMs are trained on
large amounts of unlabelled data from multiple languages so that low resource
languages may benefit from shared vocabulary and other linguistic information
from high resource languages and other similar languages in the MLM. Very few
MLMs have representations for African languages and many of those available
are trained with noisy data Adelani et al. (2021c); Alabi et al. (2020);
Kreutzer et al. (2021) which may affect downstream tasks. We provide
information about crosslingual tools in Table F.4 and other NLP models in
Table tab:modelresources.

###  F.4 Raw Data

Blog sites, online newspapers, Wikipedia, Jehovah’s Witness website are some
sources of raw data for African languages. We provide details in Table F.1 and
Table F.5.

Country | Site | Language
---|---|---
Ethiopia | Addisadmassnews | amh
Ethiopia | Ethiopian Reporter | amh and eng
Lesotho | Mosotho | sot
Namibia | Republikein | afr
Nigeria | Premiumtimes | hau
Nigeria | Leadership | hau
Nigeria | Hausa Legit | hau
Nigeria | Aminiya | hau
Nigeria | Igbo Radio | ibo
Nigeria | Kaoditaa | ibo
Nigeria | Iroyin Owuro | yor
Somalia | Boramanews | som
Somalia | Caasimada | som
Somalia | Horseedmedia | som
Somalia | Idalenews | eng and som
Somalia | Markacadeey | eng and som
Somalia | Ogaden | eng and som
Somalia | Puntlandpost | eng and som
Somalia | PQarannews | eng and som
Somalia | Shabellemedia | eng and som
Somalia | Simbanews | eng and som
Somalia | Togaherer | eng and som
Somalia | Waagacusub | eng and som
Somaliland | Dhamays news | som
Somaliland | Goobjoog | som
Somaliland | Haatuf | som
Somaliland | Maandeq | som
Somaliland | Qorilugudnews | som
Somaliland | Somalilandpost | eng and som
South Africa | Netwerk24 | afr
South Africa | Huisgenoot | afr
South Africa | Dievryburger | afr
South Africa | Isolezwe | zul
Tanzania | Mwananchi | swh
Tanzania | Nipashe | swh
Tanzania | Nipashe-Jumpaili | swh
Uganda | Bukedde | lug
Zimbabwe | Kwayedza | sna
Zimbabwe | Umthunywa | nbl
Table F.1: Newspapers in Indigenous languages of Africa.

Figure F.1: A high quality (bigger) version of the African languages map
provided in this paper.

Num Score Most Extensive Grammar Description Type # Languages $5$ long grammar
extensive description of most features of the grammar $\approx$$300+$ pages
$411$ $18.9\%$ $4$ grammar a description of most features of the grammar
$\approx$$150$ pages $243$ $11.1\%$ $3$ grammar sketch a less extensive
description of many features of the grammar $\approx$$50$ pages $562$ $25.9\%$
$2$ specific feature a description of some features of the grammar (i.e noun
class system, verb morphology, etc) $157$ $7.2\%$ $2$ phonology a description
of the sound inventory using minimal pairs $82$ $3.7\%$ $2$ dictionary
$\approx$$75+$ pages $53$ $2.4\%$ $2$ text text material $13$ $0.5\%$ $1$
wordlist $\approx$$100-200$ words $13$ $0.5\%$ $0$ minimal a small number of
morphemes $124$ $5.7\%$ $0$ overview document with meta-information about the
language (i.e where spoken, non-intelligibility to other languages etc) $48$
$2.2\%$ Total: $2,169$

Table F.2: Available linguistic resources for African languages. Adapted from
Güldemann (2018).

Model Language(s) URL Word embeddings Twi-Yorùbá
https://github.com/ajesujoba/YorubaTwi-Embedding Okwugbe (ASR) Igbo-Fon
https://github.com/bonaventuredossou/fonasr Automatic Diacritic Restoration
Yorùbá https://github.com/Niger-Volta-LTI/yoruba-adr FFR v.1.1 model Fon-
French
https://github.com/bonaventuredossou/ffr-v1/blob/master/model_train_test/fon_fr.py
Masakhane MT 30 languages https://github.com/masakhane-io/masakhane-mt
AfriBERT Afrikaans https://github.com/sello-ralethe/AfriBERT

Table F.3: A list of available models.

Language Model African Languages Represented MT5 afr, nya, mlg hau, ibo, sna,
som, sot / nso, swa, xho, yor, zul MBERT afr, swa, yor XLM-R afr, amh, hau,
gaz, som, swa, xho.

Table F.4: Language models with African languages represented.

Name Language(s) Task. References KINNEWS and KIRNEWS Corpus kin, run POS,
NER, Parsing Niyongabo et al. (2020) amh, hau, ibo, kin, lug, Masakhane NER
luo, pcm, swa, wol, yor NER Adelani et al. (2021b) Nigerian Pidgin Tweets pcm
Sentiment Ahia and Ogueji (2020) Swahili News Classification swa
Classification Amharic News classification amh Classification Azime and
Mohammed (2021) A study on African Language hau, yor NER, TC Hedderich et al.
(2020) YorùbáTwi-Embedding aka, yor NER, embedding Alabi et al. (2020) 40
languages including: amh hau ibo, XL Sum kin, gaz, pcm, som, swa, yor
Summarization Hasan et al. (2021) 282 languages including aar, afr, amh, bam,
ewe, Fula*, hau, ibo, Labelled kab, kon, kik, kua, kau, lin, mlg, ndo, nso,
gaz, run, kin, sna, som, sot, ssw, swa, WikiAnn tsn, tso, wol, xho, yor, zul
NER Pan et al. (2017) aka, amh, gaz, DARPA LORELEI som, tir NER, SemAnal
Strassel and Tracey (2016b) Automatic Diacritic Restoration yor ADR Orife et
al. (2020a) 10 languages including: afr, nya, hau, mC4 ibo, sna, som, Sotho*,
swa,xho, yor, zul LM Xue et al. (2021) Swahili Language Modeling swa LM 1600+
(including 313 Niger-Congo), The John Hopkins University Bible Corpus 67 Afro-
Asiatic, and 52 Nilo-Saharan LM McCarthy et al. (2020b) Monolingual xho corpus
swa LM Unlabelled OSCAR 166 languages including: afr, swa, yor LM Ortiz Suárez
et al. (2020) Wikipedia 37 African languages LM Alaroye yor LM gaz, amh, bam,
hau, kin, lin, VOA nbl, sna, som, swa, tir, Zimbabwe LM Jehovah’s witness More
than $100$ African languages MT Agić and Vulić (2019) gaz, amh, hau, ibo, Raw
Data BBC News run, pcm, som, swa, tir, yor LM Tanzil kab, amh, hau, som swa MT
Tiedemann (2012) Amharic Evaluation Dataset amh-eng MT Hadgu et al. (2020)
Parallel Corpora for Ethiopian Languages eng-amh, tir, gaz, wal, gez MT Abate
et al. (2018) English-Luganda Parallel Corpora eng-lug MT Back-translated
Swahili-French 1M sentence parallel data swa-fra MT Öktem et al. (2021)
Gamayun Mini kit 5k swa-eng MT Gamayun Mini kit 5k kau-eng MT English-Akuapem
Twi parallel corpus eng-aka MT afr, amh, ful, hau, ibo, kea, kam, luo, nso,
nyj, gaz, som MT Guzmán et al. (2019a) FLORES-101 swa, wol, xho, yor, zul
Guzmán et al. (2019a) Xhosa-English xho-eng MT Tiedemann (2012) Bamanankan
Lexicon bam-eng MT Autshumato eng-tsn MT efi, afr, amh, bin, ddn, fon, hau,
ibo, ish, iso, kam, kik, kmb, lin, lua, luo, nbl, nso, nya, pcm, sna, sot,
swa, tir, tiv, tsn, Masakhane MT aka, urh, ven, xho, yor, zul, swc MT Orife et
al. (2020b) Parallel Bambara Dataset bam, eng and fra MT Tapo et al. (2020)
AFRONMT eng, swa, amh, tir, gaz, som MT Lakew et al. (2020) AFROMT afr, xho,
zul, run, sot, swa, bem, lin MT Reid et al. (2021) 137 languages including
yor, afr, aka, amh, Fulfulde, ibo, som, swa, CCAligned wol, yor, zul MT El-
Kishky et al. (2020) IgboNLP ibo-eng MT Ezeani et al. (2020) MENYO-20k yor-eng
MT Adelani et al. (2021a) Extended Amharic-English bilingual corpus amh, eng
MT Gezmu et al. (2021) WikiMatrix 85 languages including swa MT Schwenk et al.
(2021) Lorelei aka MT Schwenk et al. (2021) Paracrawl 39 languages including
som and swa MT Bañón et al. (2020) 100 languages including afr, amh, Coptic,
din, ewe, kab, dop, som, swa, Parallel Bible Corpus shi, ttq, wal, wol, xho,
xuu, zul MT C&S FFR v1.1 fon-fra MT Emezue and Dossou (2020) SPCS Speech
Corpus eng, nso Speech Modipa et al. (2013) TTS data for four South African
languages afr, sot, tsn and xho Speech Mboshi French Parallel Corpus mdw, fra
Speech Godard et al. (2018) IWSLT Low Resource Shared Task swh-eng, swc-fra
Speech Anastasopoulos et al. (2021) Tico-19 swc Speech Anastasopoulos et al.
(2020) GlobalPhone hau, Swahili Speech Schultz (2002) The NCHLT Speech Corpus
afr, tso, tsn, sot, nso, of the South African languages zul, ven, ssw, xho,
nbl Speech Davel et al. (2014) ALFFA amh, swh, hau, wol Speech Gauthier et al.
(2016) Fon ASR fon Speech Laleye et al. (2016) Speech Swahili audio mini-kit
swh Speech Swahili (Congo) STT v0.3.0 swc Speech Öktem (2021) AIMS hau, lug,
kab, kin Speech Mohamud et al. (2021) amh, ibo, luo, IARPA Corpus amh, ibo,
luo, swh, zul Speech Cui et al. (2013)

Table F.5: List of available data resources. TC=Topic Classification.
C&S=Christodouloupoulos and Steedman (2015).

Lang Lang Lang Lang Lang Lang Lang Kásim,0 isekiri , 0 ndonga , 0 matuumbi, 0
bété , 0 bini, 0 babole , 0 obolo, 0 ghulfan, 0 masakin , 0 alagwa , 0 tem , 0
miisiirii , 0 gokana , 0 baga sitemu , 0 vagla , 0 mundani , 0 mbole , 0 kom ,
0 ndut, 0 gurenne , 0 hemba, 0 gbeya bossangoa , 0 seychelles creole , 0 grebo
, 0 guere , 0 majang , 0 waama , 0 bujeba , 0 ewondo , 0 mankanya , 0 emai , 0
moro , 0 lamé , 0 shatt , 0 kohumono , 0 tetela , 0 baka , 0 qafar , 0 wan , 0
talinga , 0 soninke , 0 gbaya kara , 0 yaka , 0 bororo , 0 vili , 0 tennet , 0
palor , 0 buduma , 0 balanta , 0 bai , 0 mandinka , 0 mango , 0 iraqw , 0
ajagbe , 0 bafut , 0 nubi , 0 migama , 0 burunge, 0 bobo madaré , 0 lobi , 0
yamba , 0 tera , 0 manjaku , 0 tommo so , 0 otoro , 0 shuri , 0 dyula , 0
tenyer , 0 koyraboro senni , 0 comorian , 0 duma , 0 mamvu , 0 hamer , 0 kasem
, 0 mara , 0 temne , 0 bankon , 0 kisi , 0 sama , 0 yeyi , 0 tuki , 0 kxoe , 0
guduf , 0 kwangali , 0 supyire , 0 dangaléat , 0 mofu-gudur , 0 mokilko , 0
tigré , 0 ful , 0 bandi , 0 herero , 0 !xun , 0 bangime , 0 tuareg , 0 mbe’ ,
0 mayogo , 0 ko , 0 sena , 0 chumburung , 0 bafia , 0 bori , 0 kilba , 0
avokaya , 0 ejagham , 0 londo , 0 avatime , 0 sisaala , 0 ma’di , 0 bakundu ,
0 nyimang , 0 darma , 0 tunen , 0 wolaytta , 0 mbodomo , 0 mupun , 0 kenyan
S.L , 0 gamo , 0 ciluba , 0 turkana , 0 sungor , 0 uma , 0 degema , 0 akwa , 0
aghem , 0 kpelle , 0 päri , 0 tamashek , 0 aizi , 0 katcha , 0 ijo , 0 baale ,
0 hunde , 0 samba leko , 0 ngizim , 0 príncipense , 0 nupe , 0 tumak , 0 ncàm
, 0 me’en , 0 duala , 0 ghotuo , 0 ik , 0 mwera , 0 kanakuru , 0 nsenga , 0
kera , 0 seme , 0 bidiya , 0 birri , 0 fongbe , 0 jukun , 0 burum , 0 bobangi
, 0 ekoti , 0 midob , 0 mbugu , 0 aja , 0 sukumam , 0 tama , 0 hadza , 0
ugandan S.L , 0 bushoong , 0 maninka , 0 efik , 0 kotoko , 0 kukú , 0 kunama ,
0 rundi , 0 muher , 0 mauka , 0 lua , 0 moru , 0 avikam , 0 daba , 0 mundang ,
0 dongo , 0 beembe , 0 mankon , 0 toro so , 0 krongo , 0 bamun , 0 tiv , 0
wobe , 0 miya , 0 diola-fogny , 0 mbili , 0 basaá , 0 kuanyama , 0 sebei , 0
karimojong , 0 orig , 0 budu , 0 sandawe , 0 yakoma , 0 laal , 0 pero , 0
//ani , 0 awngi , 0 kete , 0 daju , 0 lebeo , 0 leko , 0 mambwe , 0 lango , 0
hdi , 0 shinassha , 0 songe , 0 mpongwe , 0 bimoba , 0 ogbronuagum , 0 bayso ,
0 kinga , 0 acholi , 0 bilin , 0 chaga , 0 nara , 0 dizi , 0 nyanga, 0 jeli ,
0 hehe , 0 pokot , 0 burji , 0 enya , 0 mano , 0 nharo , 0 baule , 0 maasai ,
0 mondunga , 0 kagoma , 0 ngbandi , 0 lendu , 0 tirmaga , 0 leti , 0 nande , 0
runyankore , 0 shambala , 0 fyem , 0 yemsa , 0 lafofa , 0 ingessana , 0 nandi
, 0 lele , 0 senadi , 0 mituku , 0 gula iro , 0 fur , 0 kirma , 0 fe’fe’ , 0
gula , 0 niuafo’ou , 0 malgwa , 0 ebira , 0 berber , 0 ju|’hoan , 0 mono , 0
ama , 0 ngambay , 0 bura-pabir , 0 gusii , 0 bolia , 0 buli , 0 sangu , 0 ika
, 0 shabo , 0 kele , 0 kullo , 0 nkem , 0 gan , 0 beria , 0 nkonya , 0 langi ,
0 izi , 0 makonde , 0 bariba , 0 babungo , 0 kposo , 0 giziga , 0 oku , 0
mongo , 0 !xóõ , 0 jomang , 0 kenga , 0 temein , 0 Kami , 0 gorowa , 0 ding ,
0 kalanga , 0 coptic , 0 urhobo , 0 gumuz , 0 gunu , 0 bukusu , 0 dagaare , 0
uldeme , 0 gworok , 0 afar , 0 bakueri , 0 bana , 0 karó , 0 tampulma , 0
mende , 0 lunda , 0 haya , 0 nkore-kiga , 0 guinea bissau c. , 0 amele , 0
neyo , 0 bira , 0 fulfulde , 0 kanyok , 0 bahnar , 0 miri , 0 nyiha , 0 bodo ,
0 lelemi , 0 logoti , 0 mbalanhu , 0 munzombo , 0 kenyang , 0 dabida , 0 bozo,
0 karanga , 0 bisa , 0 konyagi , 0 tashlhiyt , 0 ndebele , 0 dullay , 0 mbosi
, 0 goemai , 0 murle , 0 =|hoan , 0 teso , 0 ngbaka , 0 kefa , 0 ndogo , 0
ronga , 0 tonga , 0 kresh , 0 gungbe , 0 bubi , 0 koranko , 0 konni , 0 guro ,
0 mambila , 0 mündü , 0 da’a , 0 nuer , 0 runyoro-rutooro , 0 maale , 0
dhaasanac , 0 angas , 0 harari , 0 bagiro , 0 bade , 0 ngoni , 0 ibibio , 0
pa’a , 0 mooré , 0 lozi , 0 toussian , 0 nzakara , 0 rimi , 0 zayse , 0 gimira
, 0 birom , 0 leggbó , 0 benga , 0 lagwan , 0 margi , 0 pangwa , 0 zande , 0
isoko , 0 mampruli , 0 kpan , 0 masalit , 0 konkomba , 0 gola , 0 beng , 0
maba , 0 nyangi , 0 ngemba , 0 saho, 0 suku , 0 musgu , 0 adioukrou , 0 /xam ,
0 tikar , 0 broken , 0 yana , 0 mada , 0 nuni , 0 binga , 0 kagulu , 0 ndumu ,
0 holoholo , 0 jur mödö , 0 mumuye , 0 nyamwezi , 0 shilluk , 0 ron , 0 dime ,
0 ngombe , 0 buma , 0 dahalo , 0 dhivehi , 0 kosop , 0 defaka , 0 bongo , 0
luwo , 0 lugbara , 0 koyra chiini , 0 kituba , 0 dii , 0 abidji , 0 boko , 0
komo , 0 lamnso’ , 0 klao , 0 kadugli , 0 kabiyé , 0 nyambo , 0 mbum , 0 bole
, 0 linda , 0 ila , 0 ntomba , 0 lese , 0 luvale , 0 lyele , 0 busa , 0 doko ,
0 igede , 0 aka , 0 nateni , 0 idoma , 0 kara , 0 n’ko , 0 khoekhoe , 0
rendille , 0 katla , 0 tabwa , 0 korana , 0 koh , 0 pogoro , 0 didinga , 0
luri , 0 vata , 0 podoko , 0 yulu , 0 tangale , 0 lamang , 0 engenni , 0
dadjriwalé , 0 berta , 0 tsogo , 0 dagbani , 0 bulu , 0 kiluba , 0 tarok , 0
datooga , 0 bari , 0 mungaka , 0 ega , 0 ifumu , 0 mahican , 0 gude , 0 runga
, 0 sare , 0 masa , 0 yansi , 0 mbay , 0 wéménugbé , 0 sengele , 0 kela , 0
anyi , 0 fulani , 0 mursi , 0 soddo , 0 diola-kasa , 0 jamsay , 0 koorete , 0
ga’anda , 0 arbore , 0 anywa , 0 loma , 0 fiote , 0 dyimini , 0 alladian , 0
bena-lulua , 0 mbere , 0 doyayo , 0 gidar , 0 etsako , 0 ngiti , 0 ogbia , 0
subiya , 0 mba , 0 chai , 0 tupuri , 0 kanembu , 0 tima , 0 koromfe , 0 godié
, 0 nanerge , 0 mambai , 0 koegu , 0 lucazi , 0 adamorobe S.L , 0 anufo , 0
sotho , 0 dong , 0 aari , 0 kemant , 0 kanuri , 0 sidaama , 0 donno so , 0
bemba , 0 deti , 0 lamba , 0 angolar , 0 gade , 0 gunya , 0 barambu , 0
bagirmi , 0 kamba , 0 mbara , 0 vai , 0 makaa , 0 gwari , 0 nafaanra , 0
nigerian pidgin , 0 rashad , 0 mangbetu , 0 somali , 1 igbo , 1 bambara, 1
venda , 1 tumbuka , 1 twi, 1 sango, 1 kikuyu, 1 kirundi, 1 ndonga, 1 lingala,
1 sesotho , 1 chichewa , 1 dinka , 1 malagasy , 1 ewe , 1 kinyarwanda , 1
kabiye , 1 kongo , 1 northern sotho , 1 kabyle , 1 oromo , 1 akan , 1 tsonga ,
1 luganda , 1 amharic , 2 hausa , 2 xhosa , 2 swahili , 2 zulu , 2 tswana , 2
wolof , 2 tigrinya , 2 Yorùbá , 2 afrikaans , 3

Table F.6: Language diversity index. Adapted from Joshi et al. (2020).

##  Appendix G Data Quality

The preliminary evaluation of Flores101 dataset for Yorùbá was done by a
native speaker of Yorùbá who is also a linguist. Specifically, $57\%$ of the
dataset was randomly selected while keeping track of the word’s sentential
context and the English source context. We removed all numerals written with
digits from the dataset before the random selection. This was to help us focus
on lexical items alone. We found (1) spelling errors, (2) inconsistent
spellings, which are instances of different spellings for the same word within
the text, (3) borrowed words not adapted to the orthographic convention of the
target language, without recourse to named entities, and (4) incorrect tone
marks. Further evaluation will be required to access the quality of the
dataset on a semantic and syntactic level. Examples of each of the errors
identified is presented in Table G.1.

Output Sentence Spelling errors Yorùbá Source "Mo dúpé lówó àdo tó gbórùkù ti
eléwòn bíi témi" … English Source "Thanks for those who supported a convict
like me",… Yorùbá Target Ìflrúnú hàn bèrè ní ago mókànlá (UTC+1) ní Whitehall
ní wájú enu ònà ilé isé olópàá sí òpópónà Downing, ilé áre orílè ‘ed‘e.
English Source The protest started around 11:00 local time (UTC+1) on
Whitehall opposite the police-guarded entrance to Downing Street, the Prime
Minister’s official residence. Inconsistent spellings Yorùbá Target Fidali,
omo odun-28 ti darapò mó egbé agbáboolu Basilona … English Source 28-year-old
Vidal had joined Barça … Yorùbá Target Agbábòlù Tòní ní Alex Overchkin ti
Washington Capitals. English Source Today’s Player of the Day is Alex Ovechkin
of the Washington Capitals. Yorùbá Target Kósélòmín tó seré jù tàbí je góòlù
jù fún ikò Agbábòòlù ju Bobek English Source No one else has ever made more
appearances or scored more goals for the club than Bobek. Borrowed words not
adapted to orthographic conventions of target language Yorùbá Target Àwon kan
gbàgbó pèlú john Grant, pé àti funding crunch àti sísún ní èkó ìmòye ètó orí
amóhùnmáwòrán dási láti parí eré náà. English Source It is believed by some,
including John Grant, that both the funding crunch and a shift in the
philosophy of educational television programming contributed to ending the
series. Yoruba Target Àwon onímò sáyénsì ma n pè ní “stimulated emission of
radiation" torí àwon átómsokù ma ń fura sí iná tó ràn èyí sokùn fa kí fotoni
ina maa jáde, iná dè jé irúfé rediesóni. English Source Scientists call this
process "stimulated emission of radiation" because the atoms are stimulated by
the bright light, causing the emission of a photon of light, and light is a
type of radiation. Incorrect tone marking Yorùbá Target Awon iIwe naa fihan
ile ifowopamo merinla to ran awon onisowo olola pa ilopo bilioni owo Amerika
mo lati le sa fun owo ori ati awon ofin miin. English Source The documents
showed fourteen banks helped wealthy clients hide billions of US dollars of
wealth to avoid taxes and other regulations.

Table G.1: Some errors from flores101 for Yorùbá. We indicate the errors with
bold type fonts.