M ACHINE T RANSLATION FOR A FRICAN L ANGUAGES : C OMMUNITY C REATION OF D ATASETS AND M ODELS IN U GANDA
A BSTRACT
Reliable machine translation systems are only available for a small proportion of the world’s languages, the key limitation being a shortage of training and evaluation data.
We provide a case study in the creation of such resources by NLP teams who are local to the communities in which these languages are spoken.
A parallel text corpus, SALT was created for ﬁve Ugandan languages (Luganda,
Runyankore, Acholi, Lugbara and Ateso) and various methods were explored to train and evaluate translation models.
The resulting models were found to be effective for practical translation applications even for those languages with no previous NLP data available, achieving mean BLEU score of 2 for translations to English and 9 from English.
The SALT dataset and models described are publicly available here 1 .
I NTRODUCTION
People who do not speak one of the world’s major languages have difﬁculty accessing information and resources.
Reliable machine translation systems are only available for a tiny proportion of the world’s roughly 7,000 languages ((Haddow et al., 2021)) ; while there is an increasing body of work on low-resource language modelling, the main problem is a lack of data.
The most important data typically needed for machine translation is a corpora of parallel texts, in which there are pairs of matching sentences in source and target languages.
For systems currently in practical use, the data often includes text from secondary sources, e.g. by scraping the web.
This presents a number of problems for languages in the ‘long tail’:
• Most of the content on the web is in English, and the amount of usable data turned up by webscraping typically falls off rapidly after the ﬁrst few tens of languages.
There are languages with millions of native speakers for which web scraping has not resulted in any usable training data; in many cases, even classiﬁers for language identiﬁcation is not feasible (Caswell et al., 2020).
• The use of secondary data means that models learned are subject to any biases brought about by that data.
Text from the internet can be offensive and of poor quality; but alternatively the reliance on single sources, e
g. JW300 (Agi´c & Vuli´c, 2019), only cover a speciﬁc range of topics.
• There is no reliable data for evaluating model quality.
BLEU scores, for example, could be misleading when evaluation data is low quality and subject to bias.
In this paper we describe an alternative, community-based method for creating open-source corpora, which provides a practical blueprint for assembling the necessary data resources for the several thousand ‘long tail’ languages.
This result of this work is a parallel text dataset in English and ﬁve Ugandan languages (Acholi,
Ateso, Luganda, Lugbara, and Runyankole) that we call SALT (Sunbird African Language Transla- [Table] [Table] tion dataset), as well as trained multilingual benchmark models for translating to and from English to these languages.
The dataset is summarised and compared with previously existing resources for
Ugandan languages in Table 1.
The selected languages are a representation of the main language families that are spoken in Uganda.
We emphasise the collection of multi-way parallel text, because languages in geographically neighbouring areas have similar characteristics
[Table]
which a model can usefully learn from.
While creating the SALT dataset, we placed particular emphasis on making this locally relevant, such that the corpus covers the topics and concepts that we would wish to use for translation in a practical setting.
We also discuss training multi-lingual translation models, including combining our dataset with existing corpora in these same languages.
We achieve strong practical translation performance, evaluated both quantitatively by BLEU score and qualitatively by local experts.
The dataset and models described in this work are openly available
[Table]
R ELATED WORK 2. L OW RESOURCED LANGUAGE DATA COLLECTION
The need to collect quality language data without extensive digital data resources is critical, and so innovative ways of obtaining this kind of data have been an important component of recent African machine translation work.
Masakhane (Nekoto et al., 2020) is a pan-African network of NLP enthusiasts which has collectively been looking at more participatory methods of collecting language data in sustainable ways – for example the collection of Setswana and Sepedi language data (Mari- -dataset vate et al., 2020).
Funding bodies including Lacuna 3 have also emerged to speciﬁcally support the systematic collection and maintenance of language data on the African continent.
Several other smaller efforts existed previously, led by individual researchers trying to make these low resourced languages noticeable.
This included creating pockets of speciﬁc translation data but stopping short of making them widely accessible.
In Uganda, some efforts by (Nabende, 2018), (Nandutu & Mwebaze, 2020) and (Omona & Groce, 2021) to collect local datasets can be cited.
As with these and in much of Africa, previous machine translation efforts have hinged on religious text datasets such as JW300 Agi´c & Vuli´c (2019) which have not captured the local contextual use of the languages.
2. M ODELING APPROACHES
Neural Machine Translation (NMT) had a signiﬁcant boost with the introduction of the encoderdecoder (Sutskever et al., 2014) network and speciﬁcally the implementation of this network with
RNN and Transformer architectures (Vaswani et al., 2017).
Transformers particularly allowed models pre-trained on large monolingual datasets to be ﬁne-tuned to smaller tasks with limited data for example translation with low-resource languages.
A drawback of deep learning based approaches to NMT is the amount of data and compute required to train these models successfully, particularly affecting low-resource languages.
The use of pre-trained models greatly impacted low-resource language modeling, because appreciable results could start to be obtained for complex tasks like machine translation.
For example the mBART architecture Tang et al.
(2020) implemented as a pre-trained model ﬁne-tuned on lowresource languages including Nepali, Sinhala and Gujarati shows marked improvement in performance as measured by the BLEU score.
Further improvements in modeling have been realised in multilingual models which solve the challenge of obtaining large monolingual datasets for training.
Experiments done by (Tang et al., 2021) already show that even with a dataset of 10K sentences, multilingual pre-trained models are able to achieve BLEU scores signiﬁcantly above the baseline.
D ATASET CREATION
Uganda has 43 local/native languages used by large sections of the population.
We focus on Acholi,
Ateso, Luganda, Lugbara, and Runyankole.
Table 2 gives an overview of the languages used in this research.
The selection was based on the number of speakers and also making sure that each region of the country is represented.
The numbers represent the native speakers of the language based on the (Uganda Bureau of Statistics, 2016).
While designing our data collection process, we were mindful of various challenges and potential sources of bias.
Achieving consistency can be challenging given that low-resource languages may lack norms such as standardised orthographies and have a scarcity of professional translators.
Dialects can also mean that there is no standard way of arriving at the same translation: we noted that a given concept may be expressed differently between sub-regions, even though they all nominally belong to the same language group.
Translators themselves have individual biases and idioms.
All the Ugandan languages that we consider in this work have genderless pronouns: the translations of the sentences “he went to Kampala” and “she went to Kampala” are identical.
During data collection we only asked for translations from English to Ugandan languages, however when using them to train translation systems in the other direction, this could be misleading.
Our methodology for dataset creation included recruitment and training of local translators ( § 3.1), identiﬁcation and selection of source domains and “prompts” ( § 3.2), creation of English seed sentences ( § 3.3), and ﬁnally translation and validation ( § 3.4). [Table]
3. T RANSLATOR RECRUITMENT
Given the diversity and geographical coverage of the selected languages, translators were recruited from different parts of the country.
Translators included language experts, including professional translators as well as teachers and tutors from language schools.
We placed this emphasis on experienced professionals given that the collection of parallel text corpora has a language-preservation effect; for languages with few digital data resources, any new resources carry extra weight as examples of how that language is used.
Translators were given further project-speciﬁc training to ensure consistency and quality of the translations.
For example, emphasis was put on preserving meaning and context of the source in the target sentence, and to avoid “translation-ese” by giving natural translations of the seed sentences, as someone might commonly speak or write them.
The choice of data entry and translation tools can be a barrier for some language experts.
Elderly translators had authoritative knowledge of the language but could ﬁnd it challenging to work with digitised data entry methods such as spreadsheets.
We therefore found it important to diversify the tools for translation, including not only a custom online translation management system 4 but also printing, handwriting and scanning as shown in Figure 1.
Other translators had intermittent access to the internet and needed tools that made it possible to work ofﬂine, for which spreadsheets sent by email were a workable solution.
3. P ROMPT MATERIALS
Sentences in English to be translated were created using a process of prompts, for example headlines from online news sources from different regions in Uganda.
We preferred not to use text from online sources directly for translation, as there can be issues both with copyright and with the formal tone of such text.
A diversity of sources were used for prompts, including social media and more speciﬁc material such as legal articles 5 , where although the prompt material may be technical and difﬁcult to translate, can be an inspiration for natural sentences on those topics.
Prompts were generated with a target balance of diverse topics, including but not limited to agriculture, health, and social issues.
3. G ENERATION OF E NGLISH SEED SENTENCES .
Given a list of prompt English sentences, the data collection team was able to generate new sentences that comprised our seed English dataset.
To generate a seed sentence from a prompt, one has to ensure the context is kept while rewriting the sentence to have a similar meaning to the original one.
Table 3 depicts an example of the seed sentence generation process.
A seed sentence is generated from a newspaper headline to reﬂect the gist of a comtemporary issue
in
Agriculture featured
in the newspaper Given the list of prompts, these were converted to speciﬁc sentences to be translated, according to the following principles:
• Do not include personally identifying information such as names.
Replace with ﬁctitious details, or change the sentence to not include a name.
Vary the level of formality, for example by looking at a news headline and asking ‘how would I communicate this naturally to a family member?’
• Add additional variation by changing between ﬁrst/third person point of view, active/passive voice, and so on.
• Check that the sentence is sufﬁciently different from the prompt.
The end result of this process was a list of the English sentences which would each be translated to the ﬁve Ugandan languages.
3. T RANSLATION AND QUALITY CHECKS
Translation took place in a two-step process to assure some level of quality.
The English set of seed sentences was chunked out in batches of 100 or 150 sentences and given to each translator.
Upon completing a batch the translator was responsible for passing this on to the nominated language expert who worked with the translator to correct any errors in grammar and translation.
A batch was not cleared off until it had be signed off by the language expert and checked for consistency and completeness by the overall coordinator, at which point another batch was provided to the translator.
After the whole dataset was translated, a subset of 500 sentences in every set of 2000 translated sentences was sampled and reviewed by the team to ensure completeness in the translation of all ﬁve languages and correctness of the translations.
Some judgement was required here, for example in terms of which words to leave as English and which to try to translate, such as when to attempt translation of names.
A further set of checks on the whole dataset was done by the technical team packaging the data for online release and modeling, and a back and forth process of reﬁnements was maintained until there was convergence of the dataset.
Table 4 gives examples of the dataset with translations from English seed sentences to the ﬁve local languages.
[Table]
B ENCHMARK TRANSLATION MODELS FOR U GANDAN LANGUAGES 4. T RAINING WITH OUR DATASET
A key result of our work was creation of SALT; a parallel text dataset of key Ugandan languages.
To evaluate how useful this dataset was, we developed benchmark translation models.
The scope of our experiments was restricted to translation between each of the Ugandan languages considered and English; we do not study here translation between Ugandan languages.
We use multilingual pre-trained translation models from OPUS-MT (Tiedemann et al., 2020) as a starting point, utilising the MarianMT architecture 6 and then ﬁnetuned these on our dataset.
We particularly focus on the mul-en (multiple languages to English) and en-mul (English to multiple languages) models as starting points for our work.
Both of these models have support for Luganda, but none of the other Ugandan languages in our study.
Multilingual models are particularly of interest for us here, because: (1) it is more practical in terms of computational resources during training and deployment to have fewer models covering multiple languages, and (2) we are interested in the possibility of cross-lingual transfer, in which translation performance in one language is actually improved by the availability of training data for different – but related – languages.
Multilingual modelling employs the basic concept described in (Johnson et al., 2017), that source text in training and test data can include a token specifying the desired language of the target text.
In the case of a model which supports multiple source languages but only one target language ( mul-en ) then these tokens are not needed.
We denote these target-language tags using the ISO 639-3 code for each language: >>ach<< (Acholi), >>lgg<< (Lugbara), >>lug<< (Luganda), >>run<< (Runyankole) and >>teo<< (Ateso).
These were added as special tokens to the pretrained en-mul tokenizer.
In line with the ﬁndings of (Araabi & Monz, 2020), we observe that relatively large batch sizes improve performance with limited training data.
We train with a batch size of 3000 and initial learning rate of 1e-4.
The number of training steps was determined by early stopping on validation loss.
Training multi-lingual models took approximately four hours on a single V100 GPU.
The trained models and demo are provided.
6 https://huggingface.co/docs/transformers/model_doc/marianhuggingface.co/Sunbird 8 translate.sunbird.
ai Table 6 provides results for the the baseline pretrained model and the multilingual models for translating from any of the local languages to English.
Results of these models for the reverse translation from English to any one of these local languages is depicted in Table 7.
4. A UGMENTING OUR DATASET WITH SECONDARY DATA
To improve the model we augment our dataset with publicly available datasets; the FLORESand the MT560 datasets that include one local language, Luganda for FLORES-101 and 3 local languages for MT560 as shown in Table 1.
We add these two datasets to our dataset and retrain both models; the ( mul-en ) and the en-mul .
Performance of our models with a combination of these three datasets is shown in Table 6 and Table 7.
Back-translation To extend our dataset further, we created back-translation data, using monolingual text.
Bearing in mind the problems arising with web-scraped text outlined in Section 1, we carefully selected the text sources to be used for this.
For example, rather than indiscriminately using
English text, we curated a set of locally relevant English text e.g. from local news sites, containing the terms and concepts that we believed would be of interest to prospective local users of translation models.
After ﬁltering web-scraped data for obvious poor quality sentences and incorrect language, we obtained 88.6K English, 32.5K Luganda, 6.3K Acholi, and 1.1K Ateso sentences.
We used our best en-mul model to create back-translated training data for the mul-en model and vice versa, for two iterations.
Named entities Early models that we trained had a tendency to fail with out-of-vocabulary (OOV) terms, particularly named entities.
Models would attempt to translate proper nouns that should actually be simply passed through to the output.
To help correct this tendency, we created a further dataset of named entities, in which the source and target text were identical.
Named entities were sourced from the WNUT17 (Derczynski et al., 2017) and WikiGold (Balasuriya et al., 2009) databases.
Supplementary training data created from these sources had identical text in the input and output, as shown at the bottom of Table 5, teaching the model to leave OOV terms unchanged in the output.
This had no noticeable effect on BLEU scores, but a signiﬁcant effect on subjective translation quality.
R ESULTS AND DISCUSSION
Our goal in building the dataset and models is to facilitate translation combining linguistic quality as well as contextual meaning.
We ideally wish to evaluate this with human veriﬁcation, which is underway at the time of writing this paper.
We therefore present statistical results using Bi-Lingual
Evaluation Understudy (BLEU) score metric, computed on a 10% held out test set.
While BLEU is not a perfect measure it provides a convenient indication of translation quality.
Results from our experiments are in Table 6, which depicts results of all our models with the different training data subsets for the ( mul-en ) translation task and Table 7 for the en-mul task.
We only study translation to and from English in this study, and do not present any results on translation between local languages – though we view this as an interesting future direction.
We ﬁrst note that all our results were strongly superior to the pre-trained OPUS baseline for Luganda.
Performance is highest generally for models where the largest amount of data was used to train the model, i.e. our dataset with FLORES-101, MT560 and back-translation data.
Back-translation was more effective for mul-en translation, since more English text was available and with a greater degree of consistency.
Back-translation slightly deteriorates the performance of English to Acholi and English to Runyankore.
We also show the performance achieved when training a series of pairwise models (e.g. en-ach , en-lgg , en-lug , en-nyn and en-teo rather than one en-mul ).
In all but one case, the performance of multilingual model training is better than pairwise, particularly for translation to English where we observe BLEU score improvements of +1.9 to +5.9.
This provides some evidence that cross-lingual transfer can occur for these languages, so that the model can beneﬁt from simultaneous training of multiple different-but-related languages. We hope to continue to beneﬁt from this [Table] [Table]
+ MT
, FLORES
[Table] Pairwise (including MT560, FLORES, back-translation [Table] effect by expanding the dataset with other languages in future, and merging it with other African language translation datasets.
In addition, we also hope to increase the diversity of corpus to include contexts which may currently not be captured by the current version.
C ONCLUSION
Africa has very high linguistic diversity but a paucity of digital data resources.
Public research has focused on datasets with limited scope, such as religious text, whereas data for proprietary translation systems are commonly developed by third party vendors who do not have access to the models under development.
We opted to take a community-based approach to the collecting of more contextual language data.
This allowed us to create a dataset and models which we believe to have high local relevance.
[Table]
We have created a multi-way dataset named SALT, with at least 25k sentences in each of ﬁve Ugandan languages, and trained a set of effective, publicly available benchmark models.
For some of the languages in the study, these are the ﬁrst existing machine translation resources available, to our knowledge.
Our data collection framework also makes it easily extensible to other local languages, since translation would begin from the seed English text already available.
This ﬁts into our goal of creating sustainable and easily maintainable language datasets for Africa.