Bayelemabaga: Creating Resources for B ambara NLP (2025)

Abstract: Data curation for under-resource languages enables the development of more accurate and culturally sensitive natural language processing models. However, the scarcity of well-structured multilingual datasets remains a challenge for advancing machine translation in these languages, especially for African languages. This paper focuses on creating high-quality parallel corpora that capture linguistic diversity to address this gap. We introduce Bayelemabaga, the most extensive curated multilingual dataset for machine translation in the Bambara language, the vehicular language of Mali. The dataset consists of 47K Bambara-French parallel sentences curated from 231 data sources, including short stories, formal documents, and religious literature, combining modern, historical, and indigenous languages. We present our data curation process and analyze its impact on neural machine translation by fine-tuning seven commonly used transformer-based language models, i.e., MBART, MT5, M2M-100, NLLB-200, Mistral-7B, Open-Llama-7B, and Meta-Llama3-8B on Bayelemabaga. Our evaluation on four Bambara-French language pair datasets (three existing datasets and the test set of Bayelemabaga) show up to +4.5 , +11.4 , and +0.27 in gains, respectively, on BLEU, CHRF++, and AfriCOMET evaluation metrics. We also conducted machine and human evaluations of translations from studied models to compare the machine translation quality of encoder-decoder and decoder-only models. Our results indicate that encoder-decoder models remain the best, highlighting the importance of additional datasets to train decoder-only models.

## introduction driven by the availability of massive, digitized data sets and advancements in neural architectures @xcite , state-of-the-art natural language processing (nlp) models are widely applied to the world's high-resource languages (e.g., english, french, spanish). they are employed in tasks such as machine translation (mt) @xcite , name entity recognition (ner) @xcite , automatic speech recognition (asr) @xcite . yet the vast majority of the world's languages, and by extension the people who speak these languages, lack the digitized data resources needed to support mt systems @xcite such as google translate and other important language technology applications. these under-resourced languages have yet to benefit from recent advances because they lack the large volumes of text needed to drive language technology development. the case of neural machine translation @xcite is particularly representative as it requires large volumes of parallel data between pairs of source and target languages. moreover, the available data in under-resource languages is often noisy and diverse, with non-standardized spelling, accenting, marking, multiple scripts, code-switching, etc. for example, bambara, a tonal language with a rich morphology from the mande language family foot_0 , has several competing writing systems: adjami (arabic-based), latin, and n'ko. however, as a historically oral-only language, most bambara speakers have never been taught to read or write it. as a rule, available resources don't fit standard writing systems (e.g., systems developed during colonization) or lack standard orthography or ways to express features, such as tonality, absent in colonial scripts. to cope with this problem, language experts are actively working on standardizing the existing vocabulary and coining new words to enrich the language and support automated text processing. initiatives in this direction include masakhane for african languages @xcite , the increased presence of under-resourced languages in the popular machine translation (mt) competitions of the annual conference on machine translation (wmt) @xcite , and africanlp, a workshop dedicated to african language technologies. to help alleviate the scarcity of data for machine translation of under-resourced languages, we introduce bayelemabaga, a new comprehensive dataset for machine translation that comprises 46,976 pairs of bambara and french sentences. we collected data from decades of linguistics work on bambara from inalco foot_1 's corpus bambara de reference foot_2 , aligned collected sentences in both languages, investigated their morphological structure, and curated the content to ensure adequacy for machine translation. we evaluate the adequacy of bayelemabaga by answering the following research questions (rq): bayelemabaga aims to improve the quality of translation models for the bambara language by providing a richer training resource and adaptability to other natural language processing tasks. ## related work several linguistic studies have been conducted on the bambara language, providing valuable insights into its structure @xcite , syntax @xcite , grammar (dombrowsky-hahn, 2020), and phonology @xcite . these studies serve as foundational resources for further research and resource development @xcite @xcite @xcite @xcite . while these linguistic studies are essential for understanding language, more up-to-date and accessible resources that can be utilized by a broader audience, including language learners, educators, researchers from the nlp community, and the general public, are needed. educational materials for learning bambara are relatively scarce compared to more widely taught high-resource languages, such as french or english. however, some resources exist, primarily in textbooks and language learning guides @xcite . while these materials are valuable, they may be outdated or difficult to access, particularly for learners outside academic or linguistics research settings. there is a need for more interactive and accessible educational resources that cater to different learning styles and proficiency levels. some online dictionaries and language learning apps exist @xcite but are often limited in scope or functionality. additionally, there is a lack of digital corpora or databases that could facilitate machine translation (mt), automatic speech recognition (asr), and text-to-speech (tts) @xcite . leveraging technology to create digital resources, such as interactive language learning platforms, mobile apps, and multimedia content, could significantly improve accessibility and engagement for bambara learners and speakers. various organizations and initiatives have been working to promote and preserve the bambara language, such as inalco and the academie malienne des langues (amalan) in mali, which aims to standardize and encourage the use of national languages, including bambara. however, there is a need for more comprehensive and sustained efforts to create resources that support language preservation, such as the development of educational materials, the promotion of bambara in media and literature, and the integration of the language into formal education systems @xcite . additionally, while the bambara language has a rich linguistic heritage and a significant number of speakers, the availability of resources for neural machine translation is limited compared to high-resource languages like french or english @xcite . to address the gaps and meet the growing demand to enable bambara to be a human-technology language, we put together a collaborative effort involving linguists, educators, technology experts, and community stakeholders to curate decades of linguistic data from varying sources, including books, periodicals, news, etc., for machine learning, including machine translation. ## the bayelemabaga dataset we created a parallel text dataset for the dialect continuum of mande languages spoken in west africa. our contribution focuses on the bambara language, described by tapo et al. (2020) as a tonal language (different words with different inflections convey different meanings) with a rich morphological structure, similar to other languages in the mande language family. this family consists of several languages (bambara, dyula, maninka, etc.) spoken by 30-40 million people across the african continent, among whom there are around 15-18 million bambara speakers, primarily in mali. with three central writing systems (adjami, latin, and n'ko), bambara uses diacritical marks to indicate high or low tones in the spoken language, helping distinguish between words that use the same sequence of letters. there are 27 letters in its latin writing script except for q, v, and x, commonly seen in french and english. an example of an additional character is ¢, as shown in the bambara translation of the following phrase. ## experiments we evaluate the quality of the bayelemabaga dataset by comparing its performance before and after curation using various machine translation ## conclusion in this paper, we introduced bayelemabaga, a bambara-french parallel corpora of 47k pairs of sentences collected from 231 data sources and curated to improve the quality of mt tasks. we explored the effect of curated data on mt compared to utilizing the raw dataset. we observed that bayelemabaga improves translation quality by up to +4.5, +11.4, and +0.27 on bleu, chrf++, and africomet scores. furthermore, we investigated the benefits of introducing a new data set by fine-tuning seven mt models (mbart, mt5, m2m-100, nllb-200, mistral-7b, open-llama-7b, and meta-llama3-8b) on bayelemabaga, and evaluating them on three existing bambara-french corpora. our comparisons demonstrated that models fine-tuned on bayelemabaga improve the translation quality across all datasets. we also explored the impact of our new dataset on existing encoder-decoder and decoder-only models. machine and human evaluations showed that the encoder-decoder models yield the highest quality in bambara-french and french-bambar translations. in future work, we will conduct a more detailed human evaluation to explain the performance of decoder-only models and investigate if our observations on machine translation apply to speech data, especially for primarily oral languages (pols).