Enhancing Neural Machine Translation of Indigenous Languages through Gender Debiasing and Named Entity Recognition

Gender bias is a prevalent issue in Natural Language Processing, and it can be particularly pronounced in under-represented indigenous languages. This degrades the performance of NLP applications such as machine translation and promotes unreliable prejudices. In recent years there has been growing interest in developing methods to mitigate gender bias in NLP tasks, using lexicon, dictionary, WordNet or ConceptNet. This study explores the effectiveness of gender debiasing and Named Entity Recognition in the context of indigenous languages, such as Inuktitut, an under-represented Inuit language in Canada. Experimental results showed an improvement in the performance of Inuktitut- English Neural Machine Translation.