mlsanityData to 7 Oct 2026

A Convolutional Encoder Model for Neural Machine Translation

arXiv 1611.02344 ↗First posted 7 Nov 2016cs.CLcited by 54

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Abstract (arXiv)

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.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Nov 2016.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 15; 2022: 21; 2023: 9; 2024: 5; 2025: 2; 2026: 1.

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50 most cited of 54 All 54 (JSON)