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Source code of paper "Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach" - EMNLP2018

License: Apache License 2.0

Shell 5.81% Python 87.33% Perl 6.63% Emacs Lisp 0.01% JavaScript 0.22%

ape-npi's Introduction

APE-NPI

Source code of paper "Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach" - EMNLP2018

Dependencies

  • TensorFlow 1.2+ for Python 3
  • YAML and Matplotlib modules for Python 3: sudo apt-get install python3-yaml python3-matplotlib
  • A recent NVIDIA GPU

How to use

Train a model (CONFIG is a YAML configuration file, such as config/default.yaml):

./seq2seq.sh CONFIG --train -v 

Translate text using an existing model:

./seq2seq.sh CONFIG --decode FILE_TO_TRANSLATE --output OUTPUT_FILE

or for interactive decoding:

./seq2seq.sh CONFIG --decode

APE experiments

  • Training scripts and configuration for all experiments in the paper can be found under ./experiments folder

Credits

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