Accuracy based on word position not included padding. 80% of the dataset to train, 20% of the dataset to test. Dataset is English-Vietnam, Trainset to train, validation and test set to test.

1. basic-seq2seq, test accuracy 10.34%
2. lstm-seq2seq, test accuracy 11.89%
3. gru-seq2seq, test accuracy 11.50%
4. basic-seq2seq-contrib-greedy, test accuracy 25.28%
5. lstm-seq2seq-contrib-greedy, test accuracy 33.09%
6. gru-seq2seq-contrib-greedy, test accuracy 31.28%
7. basic-birnn-seq2seq, test accuracy 12.55%
8. lstm-birnn-seq2seq, test accuracy 12.11%
9. gru-birnn-seq2seq, test accuracy 11.98%
10. basic-birnn-seq2seq-contrib-greedy, test accuracy 27.50%
11. lstm-birnn-seq2seq-contrib-greedy, test accuracy 34.25%
12. gru-birnn-seq2seq-greedy, test accuracy 32.58%
13. basic-seq2seq-luong, test accuracy 2.40%
14. lstm-seq2seq-luong, test accuracy 13.08%
15. gru-seq2seq-luong, test accuracy 7.35%
16. basic-seq2seq-bahdanau, test accuracy 13.22%
17. lstm-seq2seq-bahdanau, test accuracy 13.38%
18. gru-seq2seq-bahdanau, test accuracy 14.02%
19. basic-birnn-seq2seq-bahdanau, test accuracy 13.88%
20. lstm-birnn-seq2seq-bahdanau, test accuracy 13.16%
21. gru-birnn-seq2seq-bahdanau, test accuracy 13.47%
22. basic-birnn-seq2seq-luong, test accuracy 7.49%
23. lstm-birnn-seq2seq-luong, test accuracy 13.27%
24. gru-birnn-seq2seq-luong, test accuracy 13.76%
25. lstm-seq2seq-contrib-greedy-luong, test accuracy 45.52%
26. gru-seq2seq-contrib-greedy-luong, test accuracy 8.14%
27. lstm-seq2seq-contrib-greedy-bahdanau, test accuracy 43.88%
28. gru-seq2seq-contrib-greedy-bahdanau, test accuracy 44.13%
29. lstm-seq2seq-contrib-beam-bahdanau, test accuracy 24.49%
30. gru-seq2seq-contrib-beam-bahdanau, test accuracy 22.26%
31. lstm-birnn-seq2seq-contrib-beam-luong, test accuracy 24.15%
32. gru-birnn-seq2seq-contrib-beam-luong, test accuracy 22.32%
33. lstm-birnn-seq2seq-contrib-luong-bahdanau-beam
34. gru-birnn-seq2seq-contrib-luong-bahdanau-beam
35. bytenet-greedy
36. capsule-lstm-seq2seq-contrib-greedy
37. capsule-gru-seq2seq-contrib-greedy
38. dnc-seq2seq-bahdanau-greedy
39. dnc-seq2seq-luong-greedy
40. lstm-birnn-seq2seq-beam-luongmonotic, test accuracy 27.23%
41. lstm-birnn-seq2seq-beam-bahdanaumonotic, test accuracy 26.34%
42. memory-network-lstm-seq2seq-contrib, test accuracy 28.02%
43. attention-is-all-you-need-beam, test accuracy 37.80%
44. conv-seq2seq, test accuracy 33.73%
45. conv-encoder-lstm-decoder, test accuracy 32.91%
46. dilated-conv-seq2seq, test accuracy 33.17%
47. gru-birnn-seq2seq-greedy-residual, test accuracy 34.35%
48. google-nmt, test accuracy 33.09%
49. bert-multilanguage-transformer-decoder-beam, test accuracy 44.69%
50. xlnet-base-transformer-decoder-beam, test accuracy 28.83%

Link to repository, https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/neural-machine-translation

Link to dataset, https://github.com/stefan-it/nmt-en-vi#dataset

Discussion

1. Based on 20 epochs only.
2. Accuracy based on word positions.
3. Some results are empty because the models are slow to train, still waiting for the results.
4. Sort from shortest length to longest length and do bucketing from it will improve the accuracy.