Title: A Convolutional Encoder Model for Neural Machine Translation
Authors: Jonas Gehring, Michael Auli, David Grangier, Yann N. Dauphin
TL;DR: Investigate encoder models for translation and demonstrate that convolutions can outperform LSTMs as encoders.
Abstract:
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. 
Venue: Submitted to ICLR 2017