Papers with multi-split based reversible transformers

1 papers
Multi-split Reversible Transformers Can Enhance Neural Machine Translation (2021.eacl-main)

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Challenge: Large-scale transformers have been shown to improve neural machine translation performance but training these wider and deeper networks could be extremely memory intensive.
Approach: They propose a multi-split based reversible transformer and a backpropagation algorithm that does not need to store activations for most layers.
Outcome: The proposed model outperforms the vanilla transformer by at least 1.4 BLEU points in three datasets.

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