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A Convolutional Encoder Model For Neural Machine Translation

Jonas Gehring, Michael Auli, David Grangier, Yann N. Dauphin . Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2017 – 435 citations

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ACL Model Architecture

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.

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