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Neural machine translation (NMT) aims at solving machine translation (MT)problems using neural networks and has exhibited promising results in recentyears. However, most of the existing NMT models are shallow and there is stilla performance gap between a single NMT model and the best conventional MTsystem. In this work, we introduce a new type of linear connections, namedfast-forward connections, based on deep Long Short-Term Memory (LSTM) networks,and an interleaved bi-directional architecture for stacking the LSTM layers.Fast-forward connections play an essential role in propagating the gradientsand building a deep topology of depth 16. On the WMT'14 English-to-French task,we achieve BLEU=37.7 with a single attention model, which outperforms thecorresponding single shallow model by 6.2 BLEU points. This is the first timethat a single NMT model achieves state-of-the-art performance and outperformsthe best conventional model by 0.7 BLEU points. We can still achieve BLEU=36.3even without using an attention mechanism. After special handling of unknownwords and model ensembling, we obtain the best score reported to date on thistask with BLEU=40.4. Our models are also validated on the more difficult WMT'14English-to-German task.

Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation