91 research outputs found
A Convolutional Encoder Model for Neural Machine Translation
The prevalent approach to neural machine translation relies on bi-directional
LSTMs to encode the source sentence. In this paper we present a faster and
simpler architecture based on a succession of convolutional layers. This allows
to encode the entire source sentence simultaneously compared to recurrent
networks for which computation is constrained by temporal dependencies. On
WMT'16 English-Romanian translation we achieve competitive accuracy to the
state-of-the-art and we outperform several recently published results on the
WMT'15 English-German task. Our models obtain almost the same accuracy as a
very deep LSTM setup on WMT'14 English-French translation. Our convolutional
encoder speeds up CPU decoding by more than two times at the same or higher
accuracy as a strong bi-directional LSTM baseline.Comment: 13 page
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