Shallow-to-Deep Training for Neural Machine Translation

Abstract

Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but training an extremely deep encoder is time consuming. Moreover, why deep models help NMT is an open question. In this paper, we investigate the behavior of a well-tuned deep Transformer system. We find that stacking layers is helpful in improving the representation ability of NMT models and adjacent layers perform similarly. This inspires us to develop a shallow-to-deep training method that learns deep models by stacking shallow models. In this way, we successfully train a Transformer system with a 54-layer encoder. Experimental results on WMT'16 English-German and WMT'14 English-French translation tasks show that it is $1.4$ $\times$ faster than training from scratch, and achieves a BLEU score of $30.33$ and $43.29$ on two tasks. The code is publicly available at https://github.com/libeineu/SDT-Training/.

arXiv commentAccepted by EMNLP 2020

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• Sequence to Sequence Learning with Neural Networks
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• Convolutional Sequence to Sequence Learning
• Pay Less Attention with Lightweight and Dynamic Convolutions
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• Multi-layer Representation Fusion for Neural Machine Translation
• Understanding the Difficulty of Training Transformers

Cited by in corpus (1)

• StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking