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Register a new userModeling Profanity and Hate Speech in Social Media with Semantic Subspaces
النمذجة الألفاظ النذير والكراهية خطاب في وسائل التواصل الاجتماعي مع الفئات الفرعية الدلالية
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تعاني الكشف عن الكلام والكشف عن الألفاظ النبأ من البيانات الخاصة بالبيانات، وخاصة لغات أخرى غير الإنجليزية، بسبب الطبيعة الذاتية للمهام وتوافق التعليق التوضيحي الناتج عن الشركة الحالية.في هذه الدراسة، نقوم بتحديد الفئات الفرعية الملتزمة في وظائف Word وتمثيلات الجملة واستكشاف قدرة تعميمها على مجموعة متنوعة من المهام المستهدفة المماثلة والبعيدة في إعداد صفرية.تم إجراء هذا أحادي (ألماني) ويعرضا على المهام (الإنجليزية) ذات الصلة (باللغة الإنجليزية) عن كثب (باللغة الفرنسية) وغير ذات الصلة (العربية).نلاحظ أنه، في كل من المهام المستهدفة المماثلة والبعيدة وعلى جميع اللغات، فإن التمثيلات الفرعية القائمة على الفضاء الفرعي نقل أكثر فعالية من تمثيلات بيرت القياسية في إعداد الطلقة الصفرية، مع تحسينات بين F1 +10.9 و F1 +42.9 على خطوط الأساس عبر الكلاختبرت السيناريوهات أحادية الألوان واللغة اللغوية.
Hate speech and profanity detection suffer from data sparsity, especially for languages other than English, due to the subjective nature of the tasks and the resulting annotation incompatibility of existing corpora. In this study, we identify profane subspaces in word and sentence representations and explore their generalization capability on a variety of similar and distant target tasks in a zero-shot setting. This is done monolingually (German) and cross-lingually to closely-related (English), distantly-related (French) and non-related (Arabic) tasks. We observe that, on both similar and distant target tasks and across all languages, the subspace-based representations transfer more effectively than standard BERT representations in the zero-shot setting, with improvements between F1 +10.9 and F1 +42.9 over the baselines across all tested monolingual and cross-lingual scenarios.
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