# Lesan -- Machine Translation for Low Resource Languages

arXiv: https://arxiv.org/abs/2112.08191 · HTML: https://mlsanity.com/paper/2112.08191/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corpora](https://mlsanity.com/paper/1809.00197/) (2018)

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

- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [Low-resourced Languages and Online Knowledge Repositories: A Need-Finding Study](https://mlsanity.com/paper/2405.16669/) (2024)
- [Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models](https://mlsanity.com/paper/2305.17406/) (2023)
- [Enhancing Low-Resource Minority Language Translation with LLMs and Retrieval-Augmented Generation for Cultural Nuances](https://mlsanity.com/paper/2505.10829/) (2025)
- [Natural Language Processing for Tigrinya: Current State and Future Directions](https://mlsanity.com/paper/2507.17974/) (2025)

## Abstract (arXiv)

Millions of people around the world can not access content on the Web because most of the content is not readily available in their language. Machine translation (MT) systems have the potential to change this for many languages. Current MT systems provide very accurate results for high resource language pairs, e.g., German and English. However, for many low resource languages, MT is still under active research. The key challenge is lack of datasets to build these systems. We present Lesan, an MT system for low resource languages. Our pipeline solves the key bottleneck to low resource MT by leveraging online and offline sources, a custom OCR system for Ethiopic and an automatic alignment module. The final step in the pipeline is a sequence to sequence model that takes parallel corpus as input and gives us a translation model. Lesan's translation model is based on the Transformer architecture. After constructing a base model, back translation, is used to leverage monolingual corpora. Currently Lesan supports translation to and from Tigrinya, Amharic and English. We perform extensive human evaluation and show that Lesan outperforms state-of-the-art systems such as Google Translate and Microsoft Translator across all six pairs. Lesan is freely available and has served more than 10 million translations so far. At the moment, there are only 217 Tigrinya and 15,009 Amharic Wikipedia articles. We believe that Lesan will contribute towards democratizing access to the Web through MT for millions of people.