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Register a new userEvaluating Gender Bias in Hindi-English Machine Translation
تقييم التحيز بين الجنسين في الترجمة الهندية الإنجليزية
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مع نشر نماذج اللغة بشكل متزايد في العالم الحقيقي، من الضروري معالجة مسألة نزاهة مخرجاتها. غالبا ما تعتمد كلمة تضمين تمثيلات نماذج اللغة هذه ضمنيا ارتباطات غير مرغوب فيها تشكل تحيزا اجتماعيا داخل النموذج. تطرح طبيعة اللغات بين الجنسين مثل الهندية مشكلة إضافية في تقدير التحيز والتخفيف من التحيز، بسبب التغيير في شكل الكلمات في الجملة، بناء على جنس الموضوع. بالإضافة إلى ذلك، هناك أعمال متناثرة تتم في مجال أنظمة القياس والدولي لغات Instan. في عملنا، نحاول تقييم وتحديد التحيز بين الجنسين داخل نظام الترجمة الآلية الهندية-الإنجليزية. نقوم بتنفيذ إصدار تعديل من متري TGBI الموجود على أساس الاعتبارات النحوية له الهندية. قارننا أيضا وتتناقض مع قياسات التحيز الناتجة عن مقاييس متعددة للمظلات المدربة مسبقا وتلك التي تعلمتها نموذج الترجمة الآلي لدينا.
With language models being deployed increasingly in the real world, it is essential to address the issue of the fairness of their outputs. The word embedding representations of these language models often implicitly draw unwanted associations that form a social bias within the model. The nature of gendered languages like Hindi, poses an additional problem to the quantification and mitigation of bias, owing to the change in the form of the words in the sentence, based on the gender of the subject. Additionally, there is sparse work done in the realm of measuring and debiasing systems for Indic languages. In our work, we attempt to evaluate and quantify the gender bias within a Hindi-English machine translation system. We implement a modified version of the existing TGBI metric based on the grammatical considerations for Hindi. We also compare and contrast the resulting bias measurements across multiple metrics for pre-trained embeddings and the ones learned by our machine translation model.
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