| Challenge: | Existing work on machine translation of low-resource African languages is limited . despite advances in machine translation, there is limited work on Nigerian languages . |
| Approach: | They propose to focus on neural machine translation techniques for Nigerian languages . they outline the limitations of machine translation research on the continent . |
| Outcome: | The proposed research on Nigerian languages highlights the limitations of the current state of the art in machine translation. |
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| 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 . |
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| Challenge: | African languages are often left behind in state-of-the-art natural language processing systems and large language models. |
| Approach: | They analyze 884 research papers on NLP for African languages published over past five years . they identify key trends shaping the field and outline promising directions . |
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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
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Human or Neural Translation? (2020.coling-main)
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Shivendra Bhardwaj, David Alfonso Hermelo, Phillippe Langlais, Gabriel Bernier-Colborne, Cyril Goutte, Michel Simard
| Challenge: | a recent study shows that deep neural models have improved machine translation . identifying machine translation is still feasible, but is not yet known. |
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Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages (2020.findings-emnlp)
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AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages (2021.emnlp-main)
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| Challenge: | Existing reproducible benchmarks for machine translation are limited to high-resource or well-represented languages. |
| Approach: | They propose to use AfroMT to develop a reproducible machine translation benchmark for eight widely spoken African languages and a suite of analysis tools to take into account their unique properties. |
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Toward Machine Interpreting: Lessons from Human Interpreting Studies (2025.emnlp-main)
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| Challenge: | Current speech translation systems are static and do not adapt to real-world situations in ways human interpreters do. |
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Multilingual Neural Machine Translation (2020.coling-tutorials)
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| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
| Approach: | They will cover the latest advances in NMT approaches that leverage multilingualism . they will focus on topics such as language divergence, transfer learning and pivoting . |
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An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages (2020.lrec-1)
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| Challenge: | In this study, we explore massively multilingual low-resource neural machine translation. |
| Approach: | They propose to use Bible translations to train models with up to 1,107 source languages and create multilingual corpora varying the number and relatedness of source languages. |
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