Accelerating Multi-Exit BERT Inference via Curriculum Learning and Knowledge Distillation

Keyphrases

• Curriculum Learning 100%
• Knowledge Distillation 100%
• Bidirectional Encoder Representations from Transformers 100%
• Curriculum Knowledge 100%
• Multi-exit 100%
• Training Samples 66%
• Distillation 66%
• Training Paradigm 66%
• Intermediate Features 66%
• Textual Entailment 66%
• Early Exit 66%
• Benchmark Dataset 33%
• Sample-level 33%
• Two-level 33%
• Superior Performance 33%
• Real-world Deployment 33%
• Scholarly Attention 33%
• Feature Level 33%
• Generalization Ability 33%
• Training Process 33%
• Sample Feature 33%
• Number of Parameters 33%
• Performance Deterioration 33%
• Vanilla 33%
• Answer Selection 33%
• Distillation Learning 33%
• Performance Reduction 33%
• Multi-exit Architecture 33%

Engineering

• Experimental Result 100%
• Level Feature 100%
• Deep Layer 100%
• Performance Deterioration 100%
• Knowledge Distillation 100%

Computer Science

• Bidirectional Encoder Representations From Transformers 100%
• Knowledge Distillation 100%
• Training Sample 66%
• Experimental Result 33%
• Superior Performance 33%
• Training Process 33%