# Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study

arXiv: https://arxiv.org/abs/2004.00068 · HTML: https://mlsanity.com/paper/2004.00068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Domain-specific MT for Low-resource Languages: The case of Bambara-French](https://mlsanity.com/paper/2104.00041/) (2021)

## Abstract (arXiv)

We present novel methods for assessing the quality of human-translated aligned texts for learning machine translation models of under-resourced languages. Malian university students translated French texts, producing either written or oral translations to Bambara. Our results suggest that similar quality can be obtained from either written or spoken translations for certain kinds of texts. They also suggest specific instructions that human translators should be given in order to improve the quality of their work.