نظرا لأن الترجمة الآلية (MT) أصبحت أكثر قوة بشكل متزايد، والتي يمكن الوصول إليها، واستفادتها، فقد نمت إمكانات إدامة التحيز إلى جانب تقدمها.في حين تمت دراسة المؤشرات العلنية للحيز في الترجمة الآلية، فإننا نجادل بأن التحيزات السرية تعرض مشكلة ترسيخها.من خلال استخدام اللغة المحايدة بين الجنسين اللغة التركية واللغة الجنسية الإنجليزية، ندرس حالات التحيز بين الجنسين العلني والسرية في نماذج MT.على وجه التحديد، نقدم طريقة للتحقيق في العلامات الجنسانية غير المتماثلة.نقوم أيضا بتقييم التحيز في إسناد الشخصية وفحص الصور النمطية المهنية والشخصية من خلال مؤشرات التحيز العلنية في طرازات MT.يستكشف عملنا طبقة أعمق من التحيز في طرازات MT ويوضح الحاجة المستمرة لمنهجية متعددة التخصصات اللغوية في تطوير نموذج MT.
As Machine Translation (MT) has become increasingly more powerful, accessible, and widespread, the potential for the perpetuation of bias has grown alongside its advances. While overt indicators of bias have been studied in machine translation, we argue that covert biases expose a problem that is further entrenched. Through the use of the gender-neutral language Turkish and the gendered language English, we examine cases of both overt and covert gender bias in MT models. Specifically, we introduce a method to investigate asymmetrical gender markings. We also assess bias in the attribution of personhood and examine occupational and personality stereotypes through overt bias indicators in MT models. Our work explores a deeper layer of bias in MT models and demonstrates the continued need for language-specific, interdisciplinary methodology in MT model development.
References used
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