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الفرح العالمي مجموعة البيانات والنتائج لتصنيف العواطف عبر اللغات
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في حين أن العواطف جوانب عالمية لعلم النفس البشري، يتم التعبير عنها بشكل مختلف عبر لغات وثقافات مختلفة.نقدم مجموعة بيانات جديدة من أكثر من 530K منشورات عامة من الفيسبوك المجففة في 18 لغة، والتي تحمل تصنيفها بخمس عواطف مختلفة.باستخدام Asbeddings Bert متعدد اللغات، نوضح أن العواطف يمكن استنتاجها بشكل موثوق في الداخل وبين اللغات.يعد التعلم الصفرية النتائج الواعدة لغات الموارد المنخفضة.بعد النظريات المعمارية للعواطف الأساسية، نقدم تحليلا مفصلا لإمكانيات وحدود تصنيف العاطفة عبر اللغات.نجد أن التشابه الهيكلية والنظامي بين اللغات يسهل التعلم عبر اللغات، بالإضافة إلى التنوع اللغوي لبيانات التدريب.تشير نتائجنا إلى أن هناك القواسم المشتركة وراء التعبير عن العاطفة بلغات مختلفة.نطلق علنا البيانات المجهولية للبحث في المستقبل.
While emotions are universal aspects of human psychology, they are expressed differently across different languages and cultures. We introduce a new data set of over 530k anonymized public Facebook posts across 18 languages, labeled with five different emotions. Using multilingual BERT embeddings, we show that emotions can be reliably inferred both within and across languages. Zero-shot learning produces promising results for low-resource languages. Following established theories of basic emotions, we provide a detailed analysis of the possibilities and limits of cross-lingual emotion classification. We find that structural and typological similarity between languages facilitates cross-lingual learning, as well as linguistic diversity of training data. Our results suggest that there are commonalities underlying the expression of emotion in different languages. We publicly release the anonymized data for future research.
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