README.md

how-to

1. run prepare-dataset.ipynb.
2. run prepare-bpe.ipynb.
3. run prepare-t2t.ipynb.

Notes

1. First 200k Trainset to train, validation and test set to test.
2. Based on 20 epochs.
3. Accuracy based on BLEU.
4. RNN and Transformer parameters are not consistent.

For RNN,

size_layer = 512
num_layers = 2

For Transformer, we use BASE parameter from Tensor2Tensor.

Here we never tested what happened to RNN based models if we increase number of layers and size of layers same as Transformer BASE parameter.

5. Batch size not consistent, most of the models used 128 batch size.

Accuracy, not sorted

notebook	BLEU
1.basic-seq2seq.ipynb	6.319555e-05
2.lstm-seq2seq.ipynb	0.016924812
3.gru-seq2seq.ipynb	0.0094467895
4.basic-seq2seq-contrib-greedy.ipynb	0.005418866
5.lstm-seq2seq-contrib-greedy.ipynb
6.gru-seq2seq-contrib-greedy.ipynb	0.051461186
7.basic-birnn-seq2seq.ipynb	6.319555e-05
8.lstm-birnn-seq2seq.ipynb	0.012854616
9.gru-birnn-seq2seq.ipynb	0.0095551545
10.basic-birnn-seq2seq-contrib-greedy.ipynb	0.019748569
11.lstm-birnn-seq2seq-contrib-greedy.ipynb	0.052993
12.gru-birnn-seq2seq-contrib-greedy.ipynb	0.047413725
13.basic-seq2seq-luong.ipynb	8.97118e-05
14.lstm-seq2seq-luong.ipynb	0.053475615
15.gru-seq2seq-luong.ipynb	0.01888038
16.basic-seq2seq-bahdanau.ipynb	0.00020161743
17.lstm-seq2seq-bahdanau.ipynb	0.048261568
18.gru-seq2seq-bahdanau.ipynb	0.025584696
19.basic-birnn-seq2seq-bahdanau.ipynb	0.00020161743
20.lstm-birnn-seq2seq-bahdanau.ipynb	0.054097746
21.gru-birnn-seq2seq-bahdanau.ipynb	0.00020161743
22.basic-birnn-seq2seq-luong.ipynb
23.lstm-birnn-seq2seq-luong.ipynb	0.05320787
24.gru-birnn-seq2seq-luong.ipynb	0.027758315
25.lstm-seq2seq-contrib-greedy-luong.ipynb	0.15195806
26.gru-seq2seq-contrib-greedy-luong.ipynb	0.101576895
27.lstm-seq2seq-contrib-greedy-bahdanau.ipynb	0.15275387
28.gru-seq2seq-contrib-greedy-bahdanau.ipynb	0.13868862
29.lstm-seq2seq-contrib-beam-luong.ipynb	0.17535137
30.gru-seq2seq-contrib-beam-luong.ipynb	0.003980886
31.lstm-seq2seq-contrib-beam-bahdanau.ipynb	0.17929372
32.gru-seq2seq-contrib-beam-bahdanau.ipynb	0.1767827
33.lstm-birnn-seq2seq-contrib-beam-bahdanau.ipynb	0.19480321
34.lstm-birnn-seq2seq-contrib-beam-luong.ipynb	0.20042004
35.gru-birnn-seq2seq-contrib-beam-bahdanau.ipynb	0.1784567
36.gru-birnn-seq2seq-contrib-beam-luong.ipynb	0.0557322
37.lstm-birnn-seq2seq-contrib-beam-luongmonotonic.ipynb	0.06368613
38.gru-birnn-seq2seq-contrib-beam-luongmonotic.ipynb	0.06407658
39.lstm-birnn-seq2seq-contrib-beam-bahdanaumonotonic.ipynb	0.17586066
40.gru-birnn-seq2seq-contrib-beam-bahdanaumonotic.ipynb	0.065290846
41.residual-lstm-seq2seq-greedy-luong.ipynb	0.1475228
42.residual-gru-seq2seq-greedy-luong.ipynb	5.0574585e-05
43.residual-lstm-seq2seq-greedy-bahdanau.ipynb	0.15493448
44.residual-gru-seq2seq-greedy-bahdanau.ipynb
45.memory-network-lstm-decoder-greedy.ipynb
46.google-nmt.ipynb	0.055380445
47.transformer-encoder-transformer-decoder.ipynb	0.17100729
48.transformer-encoder-lstm-decoder-greedy.ipynb	0.049064703
49.bertmultilanguage-encoder-bertmultilanguage-decoder.ipynb	0.37003958
50.bertmultilanguage-encoder-lstm-decoder.ipynb	0.11384286
51.bertmultilanguage-encoder-transformer-decoder.ipynb	0.3941662
52.bertenglish-encoder-transformer-decoder.ipynb	0.23225775
53.transformer-t2t-2gpu.ipynb	0.36773485