Tigrinya Neural Machine Translation with Transfer Learning for Humanitarian Response
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Abstract (arXiv)
We report our experiments in building a domain-specific Tigrinya-to-English neural machine translation system. We use transfer learning from other Ge'ez script languages and report an improvement of 1.3 BLEU points over a classic neural baseline. We publish our development pipeline as an open-source library and also provide a demonstration application.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Mar 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 3.
5 papers.
- 2022A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translationcited by 98
- 2021The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translationcited by 47
- 2025Natural Language Processing for Tigrinya: Current State and Future Directionscited by 2
- 2025A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settingscited by 1
- 2025Low-Resource English-Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarkscited by 0
- 2025Low-Resource English-Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarks24 Sep 2025
- 2025Natural Language Processing for Tigrinya: Current State and Future Directions23 Jul 2025
- 2025A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings17 May 2025
- 2022A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation4 May 2022
- 2021The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation6 Oct 2021