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Register a new userLifelong Learning of Hate Speech Classification on Social Media
التعلم مدى الحياة لتصنيف الكلام الكراهية على وسائل التواصل الاجتماعي
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يفترض العمل الحالي على تصنيف نطق الكراهية الآلي أن DataSet ثابتة ويتم تعريف الفصول الدراسية مسبقا.ومع ذلك، فإن مقدار البيانات في وسائل التواصل الاجتماعي يزيد كل يوم، وتتغير الموضوعات الساخنة بسرعة، مما يتطلب من المصنفين أن تكون قادرة على التكيف باستمرار مع البيانات الجديدة دون أن ننسى المعرفة المستفادة مسبقا.هذه القدرة، المشار إليها باسم التعلم مدى الحياة، أمر حاسم لتطبيق الكلمة الحقيقية من مصنف الكلاب الكراهية في وسائل التواصل الاجتماعي.في هذا العمل، نقترح التعلم مدى الحياة لتصنيف الكلام الكراهية على وسائل التواصل الاجتماعي.لتخفيف النسيان الكارثي، نقترح استخدام تعليم التمثيل التغيرات (VRL) جنبا إلى جنب مع وحدة الذاكرة المستندة إلى LB-Soinn (توازن الحركة النمو الذاتي التزايدي التزايدي).تجريبيا، نظهر أن الجمع بين التعلم التمثيل التغيرات ووحدة الذاكرة LB-Soinn يحقق أداء أفضل من تقنيات التعلم مدى الحياة المستخدمة بشكل شائع.
Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. However, the amount of data in social media increases every day, and the hot topics changes rapidly, requiring the classifiers to be able to continuously adapt to new data without forgetting the previously learned knowledge. This ability, referred to as lifelong learning, is crucial for the real-word application of hate speech classifiers in social media. In this work, we propose lifelong learning of hate speech classification on social media. To alleviate catastrophic forgetting, we propose to use Variational Representation Learning (VRL) along with a memory module based on LB-SOINN (Load-Balancing Self-Organizing Incremental Neural Network). Experimentally, we show that combining variational representation learning and the LB-SOINN memory module achieves better performance than the commonly-used lifelong learning techniques.
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