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SEQ2EMO: تسلسل إلى نموذج تصنيف العاطفة متعددة العلامات
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تصنيف العاطفة متعددة العلامات هو مهمة مهمة في NLP وهي ضرورية للعديد من التطبيقات.في هذا العمل، نقترح نهج التسلسل إلى العاطفة (SEQ2EMO)، الذي نماذج ضمنيا علاقات العاطفة في وحدة فك ترميز ثنائية الاتجاه.تظهر التجارب في مجموعات بيانات Semeval'18 و Goemotions أن نهجنا تتفوق على الأساليب الحديثة (دون استخدام البيانات الخارجية).على وجه الخصوص، يتفوق SEQ2EMO على نهج السلسلة ذات الصلة الثنائية (BR) وسلسلة التصنيف (CC) في بيئة عادلة.
Multi-label emotion classification is an important task in NLP and is essential to many applications. In this work, we propose a sequence-to-emotion (Seq2Emo) approach, which implicitly models emotion correlations in a bi-directional decoder. Experiments on SemEval'18 and GoEmotions datasets show that our approach outperforms state-of-the-art methods (without using external data). In particular, Seq2Emo outperforms the binary relevance (BR) and classifier chain (CC) approaches in a fair setting.
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