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Register a new userMitigating Language-Dependent Ethnic Bias in BERT
تخفيف التحيز العرقي الذي تعتمد على اللغة في بيرت
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في هذه الورقة، ندرس التحيز العرقي وكيف يختلف عبر اللغات عن طريق تحليل التحيز الإثني والتخفيف من التحيز الإثني في بيرت أحادي اللغة الإنجليزية والألمانية والإسبانية والكورية والتركية والصينية.لاحظ وتحديد التحيز العرقي، ونحن نطور مترا مربعا يسمى درجة التحيز الفئرانية.ثم نقترح طريقتين للتخفيف؛أولا باستخدام نموذج متعدد اللغات، والثاني باستخدام محاذاة الكلمات السياقية من نماذج أحادية.قارن أساليبنا المقترحة مع بيرت أحادي الأجل وإظهار أن هذه الأساليب تخفف بشكل فعال التحيز العرقي.أي من الطريقتين يعملان بشكل أفضل يعتمد على مقدار موارد NLP المتاحة لهذه اللغة.نحن بالإضافة إلى تجربة اللغة العربية واليونانية للتحقق من أن أساليبنا المقترحة تعمل من أجل مجموعة متنوعة واسعة من اللغات.
In this paper, we study ethnic bias and how it varies across languages by analyzing and mitigating ethnic bias in monolingual BERT for English, German, Spanish, Korean, Turkish, and Chinese. To observe and quantify ethnic bias, we develop a novel metric called Categorical Bias score. Then we propose two methods for mitigation; first using a multilingual model, and second using contextual word alignment of two monolingual models. We compare our proposed methods with monolingual BERT and show that these methods effectively alleviate the ethnic bias. Which of the two methods works better depends on the amount of NLP resources available for that language. We additionally experiment with Arabic and Greek to verify that our proposed methods work for a wider variety of languages.
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