# Tigrinya Neural Machine Translation with Transfer Learning for Humanitarian Response

arXiv: https://arxiv.org/abs/2003.11523 · HTML: https://mlsanity.com/paper/2003.11523/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation](https://mlsanity.com/paper/2110.03036/) (2021)
- [Natural Language Processing for Tigrinya: Current State and Future Directions](https://mlsanity.com/paper/2507.17974/) (2025)
- [A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings](https://mlsanity.com/paper/2505.12116/) (2025)
- [Low-Resource English-Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarks](https://mlsanity.com/paper/2509.20209/) (2025)

## 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.