Papers with deep Transformers
Lipschitz Constrained Parameter Initialization for Deep Transformers (2020.acl-main)
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| Challenge: | Existing studies show that deep Transformers have difficulty in training even with residual connection and layer normalization. |
| Approach: | They propose a method that leverages the Lipschitz constraint on the initialization of Transformer parameters to ease the optimization difficulties caused by its multi-layer encoder/decoder structure. |
| Outcome: | The proposed model outperforms previous RNN/CNN models but fails to converge with the original computation order. |
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention (D19-1)
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| Challenge: | Existing methods to improve NLP convergence and computational overhead are limited by stacking more layers. |
| Approach: | They propose a depth-scaled initialization method which reduces parameter variance at initialization and reduces output variance of residual connections to ease gradient back-propagation. |
| Outcome: | The proposed method outperforms the base model on translation tasks with five translation directions while matching the decoding speed of the baseline model. |
Rewiring the Transformer with Depth-Wise LSTMs (2024.lrec-main)
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| Challenge: | Stacking non-linear layers allows deep neural networks to model complicated functions . but residual connections within each layer fail to fuse information from previous layers effectively . |
| Approach: | They propose a Transformer with depth-wise LSTMs connecting cascading Transformer layers and sub-layers. |
| Outcome: | The proposed model improves in English-German / French and multilingual tasks with BLEU. |