Papers with deep exit

1 papers
GAML-BERT: Improving BERT Early Exiting by Gradient Aligned Mutual Learning (2021.emnlp-main)

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Challenge: Existing approaches to improve the early exiting of natural language processing (NLP) are notoriously gigantic and slow in both training and inference.
Approach: They propose a framework for improving the early exiting of BERT by asking each exit to distill knowledge from each other.
Outcome: The proposed framework outperforms the state-of-the-art (SOTA) BERT early exiting methods on the GLUE benchmark.

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