Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages (2020.findings-emnlp)
Copied to clipboard
Wilhelmina Nekoto, Vukosi Marivate, Tshinondiwa Matsila, Timi Fasubaa, Taiwo Fagbohungbe, Solomon Oluwole Akinola, Shamsuddeen Muhammad, Salomon Kabongo Kabenamualu, Salomey Osei, Freshia Sackey, Rubungo Andre Niyongabo, Ricky Macharm, Perez Ogayo, Orevaoghene Ahia, Musie Meressa Berhe, Mofetoluwa Adeyemi, Masabata Mokgesi-Selinga, Lawrence Okegbemi, Laura Martinus, Kolawole Tajudeen, Kevin Degila, Kelechi Ogueji, Kathleen Siminyu, Julia Kreutzer, Jason Webster, Jamiil Toure Ali, Jade Abbott, Iroro Orife, Ignatius Ezeani, Idris Abdulkadir Dangana, Herman Kamper, Hady Elsahar, Goodness Duru, Ghollah Kioko, Murhabazi Espoir, Elan van Biljon, Daniel Whitenack, Christopher Onyefuluchi, Chris Chinenye Emezue, Bonaventure F. P. Dossou, Blessing Sibanda, Blessing Bassey, Ayodele Olabiyi, Arshath Ramkilowan, Alp Öktem, Adewale Akinfaderin, Abdallah Bashir
| Challenge: | 'Low-resourced'-ness is a complex problem that goes beyond data availability and reflects systemic problems in society. |
| Approach: | They propose to use machine translation to scale to low-resourced languages by using a dataset and a benchmarking system to measure their resource use. |
| Outcome: | The proposed approach allows participants without formal training to make a unique scientific contribution. |
Similar Papers
The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP (2026.acl-long)
Copied to clipboard
Sheriff Issaka, Keyi Wang, Yinka Ajibola, Oluwatumininu Samuel-Ipaye, Zhaoyi Zhang, Nicte Aguillon Jimenez, Evans Kofi Agyei, Abraham Lin, Rohan Ramachandran, Sadick Abdul Mumin, Faith Nchifor, Mohammed Shuraim Issah, Erick Rosas Gonzalez, Lieqi Liu, Sylvester Kpei, Jemimah Kusi Osei, Carlene Ajeneza, Persis Boateng, Prisca Adwoa Dufie Yeboah, Saadia Gabriel
| Challenge: | Among the approximately 7,000 languages spoken globally, fewer than 20 receive substantial attention in NLP research. |
| Approach: | They propose to use African multi-modal speech and text data to validate African multimodal models and validate them on targeted language data. |
| Outcome: | The African Languages Lab's results show that the proposed model outperforms untrained models in 31 languages and a 1B-parameter model beats the commercial system in Yoruba and Twi. |
Benchmarking Neural and Statistical Machine Translation on Low-Resource African Languages (2020.lrec-1)
Copied to clipboard
| Challenge: | a recent study has focused on languages where large amounts of resources are available. |
| Approach: | They benchmark state of the art statistical and neural machine translation systems on Somali and Swahili languages . they find that statistical machine translation and neural translation can perform similarly in low-resource scenarios . |
| Outcome: | The results show that statistical machine translation and neural machine translation perform similarly in low-resource scenarios. |
Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens (2026.findings-acl)
Copied to clipboard
Hellina Hailu Nigatu, Bethelhem Yemane Mamo, Bontu Fufa Balcha, Debora Taye Tesfaye, Elbethel Daniel Zewdie, Ikram Behiru Nesiru, Jitu Ewnetu Hailu, Senait Mengesha Yayo
| Challenge: | afan oromo, amharic, and tigrinya are low-resourced languages . they are used for training, benchmarks, news, health, and sports . afono o'mara: quantity does not guarantee quality of MT datasets . |
| Approach: | They investigate the quality of machine translation datasets for three low-resourced languages . they found a large skew towards the male gender in the datasets . |
| Outcome: | The results show that training data has large representation of political and religious text, but benchmark datasets focus on news, health, and sports. |
Machine Translation into Low-resource Language Varieties (2021.acl-short)
Copied to clipboard
| Challenge: | Current machine translation systems generate a "standard" target language, but many languages have multiple varieties that are different from the standard language. |
| Approach: | They propose a framework to rapidly adapt machine translation systems to generate different target varieties . they propose to use no parallel data to generate languages close to, but different from, the standard target language . |
| Outcome: | The proposed model improves on a system that generates Ukrainian and Belarusian in two languages with no parallel data. |
Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages (2024.naacl-long)
Copied to clipboard
Nathaniel Robinson, Raj Dabre, Ammon Shurtz, Rasul Dent, Onenamiyi Onesi, Claire Monroc, Loïc Grobol, Hasan Muhammad, Ashi Garg, Naome Etori, Vijay Murari Tiyyala, Olanrewaju Samuel, Matthew Stutzman, Bismarck Odoom, Sanjeev Khudanpur, Stephen Richardson, Kenton Murray
| Challenge: | Creole languages are used in much of Latin America, Africa and the Caribbean . a large multilingual bitext like ours has potential to build the best yet or first ever MT models for many languages . |
| Approach: | They present the largest cumulative dataset to date for Creole language MT . they provide MT models supporting all 41 Creoles in 172 translation directions . |
| Outcome: | The proposed model outperforms a genre-specific Creole MT model on its own benchmark for 23 of 34 translation directions. |
Translation or Recitation? Calibrating Evaluation Scores for Machine Translation of Extremely Low-Resource Languages (2026.acl-short)
Copied to clipboard
| Challenge: | Existing studies show that performance across low-resource settings is variable, resulting in a significant barrier for the MT community. |
| Approach: | They propose to use FRED Difficulty Metrics to contextualize reported performance across different language pairs to determine whether breakthroughs reported in other contexts are artifacts of benchmark collection. |
| Outcome: | The proposed metrics explain a significant portion of result variability rather than model capability. |
Scaling Low-Resource MT via Synthetic Data Generation with LLMs (2025.emnlp-main)
Copied to clipboard
Ona de Gibert, Joseph Attieh, Teemu Vahtola, Mikko Aulamo, Zihao Li, Raúl Vázquez, Tiancheng Hu, Jörg Tiedemann
| Challenge: | a recent study has shown that LLM-generated synthetic data can improve low-resource machine translation performance . traditional data augmentation techniques like back-translation preserve the human-written target and synthesize the other . |
| Approach: | They construct a document-level synthetic corpus from English Europarl and extend it via pivoting to 147 additional language pairs. |
| Outcome: | The proposed model can significantly improve low-resource machine translation performance even when noisy. |
Ethical Considerations for Low-resourced Machine Translation (2022.acl-srw)
Copied to clipboard
| Challenge: | a paper examines the ethical implications of machine translation for low-resourced languages . a value scenario illustrates potential harms that low-rsourced language communities may face . |
| Approach: | They propose to use Armenian as a case study to investigate ethical implications of machine translation for low-resourced languages. |
| Outcome: | The proposed model is based on a value-scenario model of machine translation for low-resourced languages . the model is used to identify potential harms that low-income speakers may face . |
Toucan: Many-to-Many Translation for 150 African Language Pairs (2024.findings-acl)
Copied to clipboard
| Challenge: | We introduce two language models with 1.2 billion and 3.7 billion parameters to improve Machine Translation (MT) for low-resource languages. |
| Approach: | They propose a set of tools to improve Machine Translation (MT) for low-resource languages with a focus on African languages. |
| Outcome: | The proposed model outperforms existing models on MT for African languages and improves translation evaluation metrics for 1K languages including African languages. |
AfriMMT-EA: Multi-domain Machine Translation for Low-Resource East African Languages (2026.findings-eacl)
Copied to clipboard
Naome A Etori, Kelechi Ezema, Nathaniel Romney Robinson, Davis David, Alfred Malengo Kondoro, Elisha Ondieki Makori, Michael Samwel Mollel, Maria Gini
| Challenge: | Recent advances in open-source large language models have demonstrated strong multilingual capabilities through data-efficient adaptation strategies. |
| Approach: | They propose to use AfriMMT-EA to refine two multilingual versions of Gemma-3 to better understand the region's linguistic and cultural diversity. |
| Outcome: | The proposed datasets comprise 54 local languages across five East African countries. |