Papers › A Convolutional Encoder Model for Neural Machine Translation

A Convolutional Encoder Model for Neural Machine Translation

7 Nov 2016ACL 2017 7arXiv:1611.02344archive 2025-07-28

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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Code

facebookresearch/fairseq mentioned on GitHubpytorchMIT report
siyuofzhou/CNNSeqToSeq mentioned on GitHubtorch report

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Tasks

Machine TranslationSentenceTranslationmodel

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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Methods

LSTMSigmoid ActivationTanh Activation

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