A Convolutional Encoder Model for Neural Machine Translation (1611.02344v3)

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.

• The paper introduces a convolutional encoder model that mitigates sequential computation issues in traditional NMT, improving translation speed and accuracy.
• The study employs rigorous theoretical analysis and extensive experiments on benchmark datasets to demonstrate competitive performance over RNN-based approaches.
• The findings highlight promising avenues for future research in efficient neural machine translation and scalable deep learning architectures.

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