Although neural machine translation has achieved promising results, it suffers from slow translation speed. The direct consequence is that a trade-off has to be made between translation quality and speed, thus its performance can not come into full play. We apply cube pruning, a popular technique to speed up dynamic programming, into neural machine translation to speed up the translation.

Wen Zhang, Liang Huang, Yang Feng, Lei Shen and Qun Liu. Speeding Up Neural Machine Translation Decoding by Cube Pruning. In Proceedings of EMNLP, 2018. [paper][code]

This system has been tested in the following environment.

• Ubuntu 16.04.1 LTS 64 bits
• Python 3.6
• Pytorch 1.0

• The training data consists of 44K sentences from the tourism and travel domain
• Validation Set was composed of the ASR devset 1 and devset 2 from IWSLT 2005
• Testing dataset is the IWSLT 2005 test set.

Name the file names of the datasets according to the variables in the wargs.py file
Both sides of the training dataset and the source sides of the validation/test sets are tokenized by using the Standford tokenizer.

• Source side: dir_data + train_prefix + '.' + train_src_suffix
• Target side: dir_data + train_prefix + '.' + train_trg_suffix

• Source side: val_tst_dir + val_prefix + '.' + val_src_suffix
• Target side:
  • One reference
    val_tst_dir + val_prefix + '.' + val_ref_suffix
  • multiple references
    val_tst_dir + val_prefix + '.' + val_ref_suffix + '0'
val_tst_dir + val_prefix + '.' + val_ref_suffix + '1'
......

• Source side: val_tst_dir + test_prefix + '.' + val_src_suffix
• Target side:
  for test_prefix in tests_prefix
  • One reference
    val_tst_dir + test_prefix + '.' + val_ref_suffix
  • multiple references
    val_tst_dir + test_prefix + '.' + val_ref_suffix + '0'
val_tst_dir + test_prefix + '.' + val_ref_suffix + '1'
......

Before training, parameters about training in the file wargs.py should be configured
run python _main.py

Assume that the trained model is named best.model.pt
Before decoding, parameters about inference in the file wargs.py should be configured, we set the option search_mode = 2 for the cube pruning decoding

• translate one sentence
  run python wtrans.py -m best.model.pt
• translate one file
  • put the test file to be translated into the path val_tst_dir + '/'
  • run python wtrans.py -m best.model.pt -i test_prefix