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Register a new userNot All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification
ليست كل السلبيات متساوية: إدراك التسمية الخسارة على نطاق واسع لتصنيف النص المحبب
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تنطوي تصنيف الحبيبات الجميلة على التعامل مع مجموعات البيانات مع عدد أكبر من الفصول مع الاختلافات الدقيقة بينهما.إن توجيه النموذج إلى التركيز على أبعاد التفريق بين هذه الفئات القابلة للتصريفية بشكل شائع هو مفتاح تحسين الأداء في مهام الحبيبات الجميلة.في هذا العمل، نقوم بتحليل ضبط النماذج الصعبة المتناقضة من النماذج المدربة مسبقا على مهام تصنيف نصية جيدة الحجم، وتصنيف العاطفة وتحليل المعنويات.قمنا بتضمين علاقات فئة على تكيفا في وظيفة موضوعية مناقصة للمساعدة في وزن الإيجابيات والسلبيات بشكل مختلف، وعلى وجه الخصوص، ترجغ السلبيات المربكة عن كثب أكثر من أمثلة سلبية أقل مماثلة.نجد أن الخسارة على علم التسمية التي تدركها تتفوق على الأساليب المتعاقبة السابقة، في وجود عدد أكبر و / أو فئات أكبر من الفئات القابلة للتصريف، وتساعد النماذج على إنتاج توزيعات الإخراج التي يتم تمييزها أكثر.
Fine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them. Guiding the model to focus on differentiating dimensions between these commonly confusable classes is key to improving performance on fine-grained tasks. In this work, we analyse the contrastive fine-tuning of pre-trained language models on two fine-grained text classification tasks, emotion classification and sentiment analysis. We adaptively embed class relationships into a contrastive objective function to help differently weigh the positives and negatives, and in particular, weighting closely confusable negatives more than less similar negative examples. We find that Label-aware Contrastive Loss outperforms previous contrastive methods, in the presence of larger number and/or more confusable classes, and helps models to produce output distributions that are more differentiated.
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