Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Review

Fuente: arXiv
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Main Authors: Ikae, Catherine, Kurpicz-Briki, Mascha
Format: Preprint
Published: 2024
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author Ikae, Catherine
Kurpicz-Briki, Mascha
author_facet Ikae, Catherine
Kurpicz-Briki, Mascha
contents Studying bias detection and mitigation methods in natural language processing and the particular case of machine translation is highly relevant, as societal stereotypes might be reflected or reinforced by these systems. In this paper, we analyze the state-of-the-art with a particular focus on European and African languages. We show how the majority of the work in this field concentrates on few languages, and that there is potential for future research to cover also the less investigated languages to contribute to more diversity in the research field.
format Preprint
id arxiv_https___arxiv_org_abs_2410_21126
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Review
Ikae, Catherine
Kurpicz-Briki, Mascha
Computation and Language
Studying bias detection and mitigation methods in natural language processing and the particular case of machine translation is highly relevant, as societal stereotypes might be reflected or reinforced by these systems. In this paper, we analyze the state-of-the-art with a particular focus on European and African languages. We show how the majority of the work in this field concentrates on few languages, and that there is potential for future research to cover also the less investigated languages to contribute to more diversity in the research field.
title Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Review
topic Computation and Language
url https://arxiv.org/abs/2410.21126