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Neural Machine Translation (NMT) is an end-to-end learning approach forautomated translation, with the potential to overcome many of the weaknesses ofconventional phrase-based translation systems. Unfortunately, NMT systems areknown to be computationally expensive both in training and in translationinference. Also, most NMT systems have difficulty with rare words. These issueshave hindered NMT's use in practical deployments and services, where bothaccuracy and speed are essential. In this work, we present GNMT, Google'sNeural Machine Translation system, which attempts to address many of theseissues. Our model consists of a deep LSTM network with 8 encoder and 8 decoderlayers using attention and residual connections. To improve parallelism andtherefore decrease training time, our attention mechanism connects the bottomlayer of the decoder to the top layer of the encoder. To accelerate the finaltranslation speed, we employ low-precision arithmetic during inferencecomputations. To improve handling of rare words, we divide words into a limitedset of common sub-word units ("wordpieces") for both input and output. Thismethod provides a good balance between the flexibility of "character"-delimitedmodels and the efficiency of "word"-delimited models, naturally handlestranslation of rare words, and ultimately improves the overall accuracy of thesystem. Our beam search technique employs a length-normalization procedure anduses a coverage penalty, which encourages generation of an output sentence thatis most likely to cover all the words in the source sentence. On the WMT'14English-to-French and English-to-German benchmarks, GNMT achieves competitiveresults to state-of-the-art. Using a human side-by-side evaluation on a set ofisolated simple sentences, it reduces translation errors by an average of 60%compared to Google's phrase-based production system.

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation