WMT’16 English-Romanian | Encoder | Vocabulary | BLEU |
(Sennrich et al., 2016a) | BiGRU | BPE 90K | 28.1 |
Single-layer decoder | BiLSTM | 80K | 27.5 |
Convolutional | 80K | 27.1 | |
Deep Convolutional 8/4 | 80K | 27.8 | |
WMT’15 English-German | Encoder | Vocabulary | BLEU |
(Jean et al., 2015) RNNsearch-LV | BiGRU | 500K | 22.4 |
(Chung et al., 2016) BPE-Char | BiGRU | Char 500 | 23.9 |
(Yang et al., 2016) RNNSearch + UNK replace | BiLSTM | 50K | 24.3 |
+ recurrent attention | BiLSTM | 50K | 25.0 |
Single-layer decoder | BiLSTM | 80K | 23.5 |
Deep Convolutional 8/4 | 80K | 23.6 | |
Two-layer decoder | Two-layer BiLSTM | 80K | 24.1 |
Deep Convolutional 15/5 | 80K | 24.2 | |
WMT’14 English-French (12M) | Encoder | Vocabulary | BLEU |
(Bahdanau et al., 2015) RNNsearch | BiGRU | 30K | 28.5 |
(Luong et al., 2015b) Single LSTM | 6-layer LSTM | 40K | 32.7 |
(Jean et al., 2014) RNNsearch-LV | BiGRU | 500K | 34.6 |
(Zhou et al., 2016) Deep-Att | Deep BiLSTM | 30K | 35.9 |
Single-layer decoder | BiLSTM | 30K | 34.3 |
Deep Convolutional 8/4 | 30K | 34.6 | |
Two-layer decoder | 2-layer BiLSTM | 30K | 35.3 |
Deep Convolutional 20/5 | 30K | 35.7 |