A Convolutional Encoder Model for Neural Machine Translation

Understand

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.