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

2020/03/31 by Michael Leventhal, Leventhal, Michael, Allahsera Auguste Tapo +7
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques

paper · pdf · doi:10.48550/arxiv.2004.00068

openalex publication_date 2020/03/31 · openalex created_date 2020/04/10 · openalex updated_date 2026/07/28

Abstract

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.

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