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The general trend in NLP is towards increasing model capacity and performancevia deeper neural networks. However, simply stacking more layers of the popularTransformer architecture for machine translation results in poor convergenceand high computational overhead. Our empirical analysis suggests thatconvergence is poor due to gradient vanishing caused by the interaction betweenresidual connections and layer normalization. We propose depth-scaledinitialization (DS-Init), which decreases parameter variance at theinitialization stage, and reduces output variance of residual connections so asto ease gradient back-propagation through normalization layers. To addresscomputational cost, we propose a merged attention sublayer (MAtt) whichcombines a simplified averagebased self-attention sublayer and theencoderdecoder attention sublayer on the decoder side. Results on WMT and IWSLTtranslation tasks with five translation directions show that deep Transformerswith DS-Init and MAtt can substantially outperform their base counterpart interms of BLEU (+1.1 BLEU on average for 12-layer models), while matching thedecoding speed of the baseline model thanks to the efficiency improvements ofMAtt.

Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention