Gender Bias in Machine Translation: Analyzing Linguistic Choices in Automated Systems
DOI:
https://doi.org/10.31185/wjfh.Vol21.Iss4.1307Keywords:
Keywords: Gender bias; Machine translation; Literary translation; Vinay and Darbelnet: direct and adaptive techniques.Abstract
The central purpose of this study is to analyze linguistic choices in automated systems, with a focus on gender bias in machine translation. The research applies Vinay and Darbelnet's model of translation strategies (1995) to identify variances between direct (literal) and oblique (adaptive) techniques in excerpts from Charles Dickens's Great Expectations translation into Arabic. The study examines how automated systems treat gendered language, emphasizing their leaning to sustain such biases, and deviate from the original text intent. Direct translation frequently results in gender errors and distorts the look of a character, especially for women. These findings highlight the necessity of advancing translation technologies that involve cultural comprehension, linguistic sensitivity, and moral responsibility to reduce disfigurement and improve the effectiveness of intercultural communication.
Keywords: Gender bias; Machine translation; Literary translation; Vinay and Darbelnet: direct and adaptive techniques.
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