Misgendering and Assuming Gender in Machine Translation when Working with Low-Resource Languages

arXiv:2401.13165v3 Announce Type: replace
Abstract: This chapter focuses on gender-related errors in machine translation (MT) in the context of low-resource languages. We begin by explaining what low-resource languages are, examining the inseparable social and computational factors that create such linguistic hierarchies. We demonstrate through a case study of our mother tongue Bengali, a global language spoken by almost 300 million people but still classified as low-resource, how gender is assumed and inferred in translations to and from the high(est)-resource English …

abstract arxiv case case study computational context cs.cl errors gender languages low machine machine translation social study through translation type