Democratizing Machine Translation: A CPU-Centric Training Pipeline for Low-Resource Languages (A Kalabari Case Study)

Abdou Mohamed, N., Erraji, Z., Bahafid, A., & Benelallam, I. (2024). Harnessing transfer learning from Swahili: Advancing solutions for Comorian dialects (arXiv:2412.12143). arXiv. https://arxiv.org/abs/2412.12143 Adelani, D. I. (2022). Natural language processing for African languages [Doctoral dissertation, Saarland University]. Saarländische Universitäts- und Landesbibliothek. https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/36297 Akinfaderin, A. (2020). HausaMT v1.0: Towards English–Hausa neural machine translation. In Proceedings of the Fourth Widening Natural Language Processing Workshop (pp. 144–147). Association for Computational Linguistics. https://aclanthology.org/2020.winlp-1.38/ Alabi, J. O., Azime, I. A., Zhang, M., España-Bonet, C., Bawden, R., Zhu, D., Adelani, D. I., Odoje, C. O., Akinade, I., Maab, I., David, D., Muhammad, S. H., Putini, N., Ademuyiwa, D. O., Caines, A., & Klakow, D. (2025). AFRIDOC-MT: Document-level MT corpus for African languages. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 27770–27806). Association for Computational Linguistics. https://aclanthology.org/2025.emnlp-main.1413/ Ali, F. D. M. A., Caines, A., & Malavi, J. L. A. (2021). Towards a parallel corpus of Portuguese and the Bantu language Emakhuwa of Mozambique (arXiv:2104.05753). arXiv. https://arxiv.org/abs/2104.05753 Arivazhagan, N., Bapna, A., Firat, O., Lepikhin, D., Johnson, M., Krikun, M., Chen, M. X., Cao, Y., Foster, G., Cherry, C., Macherey, W., Chen, Z., & Wu, Y. (2019). Massively multilingual neural machine translation in the wild: Findings and challenges (arXiv:1907.05019). arXiv. https://arxiv.org/abs/1907.05019 Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015). https://arxiv.org/abs/1409.0473 Belay, T. D., Tonja, A. L., Kolesnikova, O., Yimam, S. M., Ayele, A. A., Haile, S. B., Sidorov, G., & Gelbukh, A. (2022). The effect of normalization for bi-directional Amharic- English neural machine translation. In 2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA) (pp. 84–89). IEEE. https://arxiv.org/abs/2210.15224 Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922 Chen, W.-R., Adebara, I., & Abdul-Mageed, M. (2024). Interplay of machine translation, diacritics, and diacritization. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Vol. 1, pp. 7559–7601). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.naacl-long.420 Chiang, D. (2005). A hierarchical phrase-based model for statistical machine translation. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05) (pp. 263–270). Association for Computational Linguistics. https://doi.org/10.3115/1219840.1219873 Chimoto, E. A., & Bassett, B. A. (2022). Very low resource sentence alignment: Luhya and Swahili. In Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022) (pp. 1–8). Association for Computational Linguistics. https://aclanthology.org/2022.loresmt-1.1/ IJCSMT Choudhury, M., & Deshpande, A. (2021). How linguistically fair are multilingual pre-trained language models? Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 12710–12718. https://doi.org/10.1609/aaai.v35i14.17505 Dossou, B. F. P., & Emezue, C. C. (2021). OkwuGbé: End-to-end speech recognition for Fon and Igbo. In Proceedings of the Fifth Workshop on Widening Natural Language Processing (pp. 1–4). Association for Computational Linguistics. https://aclanthology.org/2021.winlp-1.1/ Ekle, O. A., & Das, B. (2025). Low-resource neural machine translation using recurrent neural networks and transfer learning: A case study on English-to-Igbo (arXiv:2504.17252). arXiv. https://arxiv.org/abs/2504.17252 Emezue, C. C., & Dossou, B. F. P. (2020). FFR v1.1: Fon-French neural machine translation. In Proceedings of the Fourth Widening Natural Language Processing Workshop (pp. 83–87). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.winlp-1.21 Ezeani, I., Rayson, P., Onyenwe, I., Uchechukwu, C., & Hepple, M. (2020). Igbo-English machine translation: An evaluation benchmark (arXiv:2004.00648). arXiv. https://arxiv.org/abs/2004.00648 Gezmu, A. M., Nürnberger, A., & Bati, T. B. (2022). Extended parallel corpus for Amharic- English machine translation. In Proceedings of the Thirteenth Language Resources and Evaluation Conference (pp. 6644–6653). European Language Resources Association. https://aclanthology.org/2022.lrec-1.716/ Hacheme, G. (2021). English2Gbe: A multilingual machine translation model for {Fon/Ewe}Gbe (arXiv:2112.11482). arXiv. https://arxiv.org/abs/2112.11482 Haddow, B., Bawden, R., Miceli Barone, A. V., Helcl, J., & Birch, A. (2021). Survey of low- resource machine translation (arXiv:2109.00486). arXiv. https://doi.org/10.48550/arXiv.2109.00486 Keita, M. K., Zampieri, M., Homan, C. M., Bremang, A. A., Asamoah Owusu, D., & Le, H. (2026). Grammatical error correction for low-resource languages: The case of Zarma. In Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026) (pp. 98–109). Association for Computational Linguistics. https://aclanthology.org/2026.loreslm-1.9/ Klein, G., Kim, Y., Deng, Y., Senellart, J., & Rush, A. M. (2017). OpenNMT: Open-source toolkit for neural machine translation. arXiv preprint arXiv:1701.02810. Kornai, A. (2013). Digital language death. PLoS ONE, 8(10), Article e77056. https://doi.org/10.1371/journal.pone.0077056 Liu, Y., Gu, J., Goyal, N., Li, X., Edunov, S., Ghazvininejad, M., Lewis, M., & Zettlemoyer, L. (2020). Multilingual denoising pre-training for neural machine translation. Transactions of the Association for Computational Linguistics, 8, 726–742. https://doi.org/10.1162/tacl_a_00343 Luong, M.-T., Pham, H., & Manning, C. D. (2015). Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 1412–1421). Association for Computational Linguistics. https://doi.org/10.18653/v1/D15-1166 Mager, M., Bhatnagar, R., Neubig, G., Vu, N. T., & Kann, K. (2023). Neural machine translation for the indigenous languages of the Americas: An introduction. In Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP) (pp. 109–133). Association for Computational Linguistics. https://aclanthology.org/2023.americasnlp-1.13/ IJCSMT Magueresse, A., Carles, V., & Heetderks, E. (2020). Low-resource languages: A review of past work and future challenges (arXiv:2006.07264). arXiv. https://arxiv.org/abs/2006.07264 Nasir, M. U., & Mchechesi, I. A. (2022). Geographical distance is the new hyperparameter: A case study of finding the optimal pre-trained language for English-isiZulu machine translation. In Proceedings of the Workshop on Multilingual Information Access (pp. 1–8). Association for Computational Linguistics. https://aclanthology.org/2022.mia-1.1/ Neubig, G., & Hu, J. (2018). Rapid adaptation of neural machine translation to new languages. arXiv preprint arXiv:1808.08416. Nyoni, E., & Bassett, B. A. (2021). Low-resource neural machine translation for Southern African languages (arXiv:2104.00366). arXiv. https://arxiv.org/abs/2104.00366 Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002). Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics (pp. 311-318). Popović, M. (2015). chrF: character n-gram F-score for automatic MT evaluation. In Proceedings of the Tenth Workshop on Statistical Machine Translation (pp. 392-395). Reid, M., Hu, J., Neubig, G., & Matsuo, Y. (2021). AfroMT: Pretraining strategies and reproducible benchmarks for translation of 8 African languages. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 1306– 1320). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.99 Shi, Y., Wang, Y., & Li, J. (2022). A review of low-resource neural machine translation. Artificial Intelligence Review, 55(4), 3241-3289. Singh, P. R., Prasad, K., Zaki, M., & Wasnik, P. (2025). In-domain African languages translation using LLMs and multi-armed bandits (arXiv:2505.15069). arXiv. https://arxiv.org/abs/2505.15069 Tapo, A. A., Coulibaly, B., Diarra, S., Homan, C., Kreutzer, J., Luger, S., Nagashima, A., Zampieri, M., & Leventhal, M. (2020). Neural machine translation for extremely low- resource African languages: A case study on Bambara. In Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages (pp. 23–32). Association for Computational Linguistics. https://aclanthology.org/2020.loresmt-1.3/ Toadoum Sari, S., Fan, A., & Logamou Seknewna, L. (2023). Ngambay-French neural machine translation (sba-Fr). In Proceedings of the First Workshop on NLP Tools and Resources for Translation and Interpreting Applications (pp. 39–47). Association for Computational Linguistics. https://aclanthology.org/2023.nlp4tia-1.6/ Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 5998–6008). Curran Associates. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa- Abstract.html Vegi, P., Sivabhavani, J., Paul, B., Prasanna, K. R., & Viswanathan, C. (2022). ANVITA- African: A multilingual neural machine translation system for African languages. In Proceedings of the Seventh Conference on Machine Translation (pp. 1090– 1097). Association for Computational Linguistics. https://aclanthology.org/2022.wmt- 1.106/ Vincent, O., Nderu, L., Ogada, K., & Mwalili, T. (2023). Neural machine translation for low resource Bantu languages in East and Southern Africa [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-3772553/v1 IJCSMT Yamoah, K. A., Weako, J., & Dorley, E. J. (2025). Building a functional machine translation corpus for Kpelle. In Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025) (pp. 52–63). Association for Computational Linguistics. https://aclanthology.org/2025.africanlp-1.8/ Zoph, B., Yuret, D., May, J., & Knight, K. (2016). Transfer learning for low-resource neural machine translation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 1568–1575). Association for Computational Linguistics. https://doi.org/10.18653/v1/D16-1163