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Register a new userhBERT + BiasCorp - Fighting Racism on the Web
هبيرت + BIASCORP - مكافحة العنصرية على شبكة الإنترنت
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لا تزال العنصرية الدقيقة والعلانية موجودة في المجتمعات المادية والإنترنت اليوم وتأثرت في العديد من الأرواح في قطاعات مختلفة من المجتمع. في هذه القطعة القصيرة من العمل، نقدم كيف نتعامل مع هذه القضية المجتمعية مع معالجة اللغة الطبيعية. نحن نفرج BIASCORP، مجموعة بيانات تحتوي على 139،090 تعليقات وقطاع أخبار من ثلاثة مصادر محددة - Fox News، Breitbartnews و YouTube. الدفعة الأولى (45000 المشروح يدويا) جاهز للنشر. نحن حاليا في المرحلة الأخيرة من وصف مجموعة البيانات المتبقية يدويا باستخدام Amazon Mechanical Turk. تم استخدام بيرت على نطاق واسع في العديد من المهام المصب. في هذا العمل، نقدم هيرت، حيث نقوم بتعديل طبقات معينة من نموذج برت المحدد مع طبقة Hopfield الجديدة. تعميم هيرت جيدا عبر توزيعات مختلفة مع ميزة إضافية من تعقيد نموذج مخفض. نحن نطلق أيضا مكتبة JavaScript 3 وطلب امتداد Chrome، لمساعدة المطورين على الاستفادة من نموذجنا المدربين في تطبيقات الويب (يقول تطبيق الدردشة) وللمستخدمين لتحديد وتقرير محتويات منحازة عنصري على الويب على التوالي
Subtle and overt racism is still present both in physical and online communities today and has impacted many lives in different segments of the society. In this short piece of work, we present how we're tackling this societal issue with Natural Language Processing. We are releasing BiasCorp, a dataset containing 139,090 comments and news segment from three specific sources - Fox News, BreitbartNews and YouTube. The first batch (45,000 manually annotated) is ready for publication. We are currently in the final phase of manually labeling the remaining dataset using Amazon Mechanical Turk. BERT has been used widely in several downstream tasks. In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer. hBert generalizes well across different distributions with the added advantage of a reduced model complexity. We are also releasing a JavaScript library 3 and a Chrome Extension Application, to help developers make use of our trained model in web applications (say chat application) and for users to identify and report racially biased contents on the web respectively
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