Neural Machine Translation for Extremely Low-Resource A frican Languages: A Case Study on B ambara (2020)

Abstract: Low-resource languages present unique challenges to (neural) machine translation. We discuss the case of Bambara, a Mande language for which training data is scarce and requires significant amounts of pre-processing. More than the linguistic situation of Bambara itself, the socio-cultural context within which Bambara speakers live poses challenges for automated processing of this language. In this paper, we present the first parallel data set for machine translation of Bambara into and from English and French and the first benchmark results on machine translation to and from Bambara. We discuss challenges in working with low-resource languages and propose strategies to cope with data scarcity in low-resource machine translation (MT).

## introduction underresourced languages, from a natural language processing (nlp) perspective, are those lacking the resources (large volumes of parallel bitexts) needed to support state-of-the-art performance on nlp problems like machine translation, automated speech recognition, or named entity recognition. yet the vast majority of the world's languages-representing billions of native speakers worldwide-are underresourced. and the lack of available training data in such languages usually reflects a broader paucity of electronic information resources accessible to their speakers. for instance, there are over six million wikipedia articles in english but fewer than sixty thousand in swahili and fewer than seven hundred in bambara, the vehicular and most widely-spoken native language of mali that is the subject of this paper. 1 consequently, only 53% of the worlds 1 meta.wikimedia.org/wiki/list_of_ wikipedias. fueled by data, advances in hardware technology, and deep neural models, machine translation (nmt) has advanced rapidly over the last ten years. researchers are beginning to investigate the effectiveness of (nmt) low-resource languages, as in recent wmt 2019 and wmt 2020 tasks @xcite , and in underresourced african languages. most prominently, the masakhane (∀ et al., 2020) community foot_1 , a grassroots initiative, has developed open-source nmt models for over 30 african languages on the base of the jw300 corpus @xcite , a parallel corpus of religious texts. since african languages cover a wide spectrum of linguistic phenomena and language families @xcite , individual development of translations and resources for selected languages or language families are vital to drive the overall progress. just within the last year, a number of dedicated studies have significantly improved the state of african nmt: @xcite analyzed the depth of transformers specifically for low-resource translation of south-african languages, based on prior studies by @xcite on the autshumato corpus (groenewald and du @xcite . dossou and emezue (2020) developed an mt model and compiled resources for translations between fon and french, @xcite modeled translations between english and hausa, orife (2020) for four languages of the edoid language family, and ahia and ogueji (2020) investigated supervised vs. unsupervised nmt for nigerian pidgin. in this paper, we present the first parallel data set for machine translation of bambara into and from english and french and the first benchmark results on machine translation to and from bambara. we discuss challenges in working with low-resource languages and propose strategies to cope with data scarcity in low-resource mt. we discuss the sociocultural context of bambara translation and its implications for model and data development. finally, we analyze our best-performing neural models with a small-scale human evaluation study and give recommendations for future development. we find that the translation quality on our in-domain data set is acceptable, which gives hope for other languages that have previously fallen under the radar of mt development. we released our models and data upon publication 4 . our evaluation setup may serve as benchmark for an extremely challenging translation task. ## the bambara language bambara is the first language of five million people and the second language of approximately ten million more. most of its speakers are members of bambara ethnic groups, who live throughout the african continent. approximately 30-40 million people speak some language in the mande family of languages, to which bambara belongs @xcite . bambara is a tonal language with a rich morphology. over the years, several competing writing systems have developed, however, as an historically predominately oral language, a majority of bambara speakers have never been taught to read or write the standard form of the language. many are incapable of reading or writing the language at all. the standardization of words and the coinage of new ones are still works in progress; this poses challenges to automated text processing. during muslim expansion and french colonization, arabic and french mixed with local languages, resulting in a lingua franca, e.g., urban bambara. most of the existing bambara resources are cultural (folk stories or news/topical) or come from social media or text messages, and these are a written in a melange of french, bambara and arabic. consequently, corpora based on common bambara usage must account for the code switching found in these mixtures. most of these characteristics are shared with related languages, e.g., a subset of the mande family of languages, where many languages are mutually intelligible. thus, our hope is that our approach will be transferable to the other twelve official local languages of mali, or to other african languages with a comparable socio-cultural and linguistic embedding, for example wolof (non-mande), which is comparable in terms of number of speakers, borrowings from arabic and french influence, and oral traditions. the next section will provide more details on digital resources and describe the process of exploring and collecting data and choosing parallel corpora for the training of the nmt model. ## conclusion our study constitutes the first attempt of modeling automatic translation for the extremely lowresource language of bambara. we identified challenges for future work, such as the development of alignment tools for small-scale datasets, and the need for a general domain evaluation set. the current limitation of processing written text as input might furthermore benefit from the integration of spoken resources through speech recognition or speech translation, since bambara is primarily spoken and the lack of standardization in writing complicates the creation of clean reference sets and consistent evaluation.