PyTorch Dual Learning
This is the PyTorch implementation for Dual Learning for Machine Translation.
The NMT models used as channels are heavily depend on pcyin/pytorch_nmt.
Usage
You shall prepare these models for dual learning step:
- Language Models x 2
- Translation Models x 2
Warm-up Step
Dual Learning Step
During the reinforcement learning process, it will gain rewards from language models and translation models, and update the translation models.
You can find more details in the paper.
- Training
You can simply use this script, you have to modify the path and name to your models. - Test
To use the trained models, you can just treat it as NMT models.
Test (Basic)
Firstly, we trained our basic model with 450K bilingual pair, which is only 10% data, as warm-start. Then, we set up a dual-learning game, and trained two models using reinforcement technique.
Configs
-
Reward
- language model reward: average over square rooted length of string
- final reward:
rk = 0.01 x r1 + 0.99 x r2
-
Optimizer
torch.optim.SGD(models[m].parameters(), lr=1e-3, momentum=0.9)
Results
-
English-Deutsch
- after 600 iterations
BLEU = 21.39, 49.1/26.8/17.6/12.2
- after 1200 iterations
BLEU = 21.49, 48.6/26.6/17.4/12.0
- after 600 iterations
-
Deutsch-English
- after 600 iterations
BLEU = 25.89, 56.0/32.8/22.3/15.8
- after 1200 iterations
BLEU = 25.94, 55.9/32.7/22.2/15.8
- after 600 iterations
Comparisons
Model | Original | iter300 | iter600 | iter900 | iter1200 | iter1500 | iter3000 | iter4500 | iter6600 |
---|---|---|---|---|---|---|---|---|---|
EN-DE | 20.54 | 21.27 | 21.39 | 21.49 | 21.46 | 21.49 | 21.56 | 21.62 | 21.60 |
EN-DE (bleu) | 21.42 | 21.57 | 21.55 | 21.55 | |||||
DE-EN | 24.69 | 25.90 | 25.89 | 25.91 | 26.03 | 25.94 | 26.02 | 26.18 | 26.20 |
DE-EN (bleu) | 25.96 | 26.25 | 26.22 | 26.18 |