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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