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Register a new userDimensional Emotion Detection from Categorical Emotion
الكشف عن العاطفة الأبعاد من العاطفة الفئوية
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نقدم نموذجا للتنبؤ بمشاعر غرامة على طول الأبعاد المستمرة من التكافؤ والإثارة والهيمنة (VAD) مع وجود شرح عاطفي قاطع. يتم تدريب طرازنا عن طريق تقليل فقدان EMD (مسافة تحالف الأرض) بين توزيع النتيجة VAD المتوقعة وتوزيع العاطفة الفئوية التي تم فرزها على طول VAD، ويمكن أن تصنف في وقت واحد فئات العاطفة وتتنبؤ بعشرات VAD للحصول على عقوبة معينة. نحن نستخدم Roberta-Large Roberta المدربة مسبقا على ثلاثة كوربورا مختلفة مع ملصقات واضحة وتقييم على Emobank Corpus مع درجات VAD. نظهر أن نهجنا يصل إلى أداء قابلا للمقارنة مع وجود أحدث من المصنفات في تصنيف العاطفة الفئوية ويظهر ارتباطا إيجابيا كبيرا مع درجات فاد للحقيقة الأرضية. أيضا، يؤدي المزيد من التدريب مع الإشراف على تسميات VAD إلى تحسين الأداء خاصة عندما تكون مجموعة البيانات الصغيرة. نقدم أيضا أمثلة على تنبؤات كلمات العاطفة المناسبة التي ليست جزءا من التعليقات التوضيحية الأصلية.
We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations. Our model is trained by minimizing the EMD (Earth Mover's Distance) loss between the predicted VAD score distribution and the categorical emotion distributions sorted along VAD, and it can simultaneously classify the emotion categories and predict the VAD scores for a given sentence. We use pre-trained RoBERTa-Large and fine-tune on three different corpora with categorical labels and evaluate on EmoBank corpus with VAD scores. We show that our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification and shows significant positive correlations with the ground truth VAD scores. Also, further training with supervision of VAD labels leads to improved performance especially when dataset is small. We also present examples of predictions of appropriate emotion words that are not part of the original annotations.
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