Papers › A Convolutional Encoder Model for Neural Machine Translation
A Convolutional Encoder Model for Neural Machine Translation
Jonas Gehring, Michael Auli, David Grangier, Yann N. Dauphin
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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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Machine Translation | IWSLT2015 German-English | Conv-LSTM (deep+pos) | BLEU score | 30.4 | #7 of 15 | Archive leaderboard | report |
| Machine Translation | WMT2014 English-French | Deep Convolutional Encoder; single-layer decoder | BLEU score | 35.7 | #46 of 57 | Archive leaderboard | report |
| Machine Translation | WMT2016 English-Romanian | Deep Convolutional Encoder; single-layer decoder | BLEU score | 27.8 | #14 of 21 | Archive leaderboard | report |
| Machine Translation | WMT2016 English-Romanian | BiLSTM | BLEU score | 27.5 | #15 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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