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  • Created almost 7 years ago
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Repository Details

Implementation of Dual Learning NMT on PyTorch

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
  • Language Models
    Check here lm/
  • Translation Models
    Check here nmt/
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
      
  • 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
      
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