seq2seq

Attention-based sequence to sequence learning

Dependencies

• TensorFlow for Python 3
• YAML and Matplotlib modules for Python 3: sudo apt-get install python3-yaml python3-matplotlib

How to use

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

python3 -m translate CONFIG --train -v 

Translate text using an existing model:

python3 -m translate CONFIG --decode FILE_TO_TRANSLATE --output OUTPUT_FILE

or for interactive decoding:

python3 -m translate CONFIG --decode

Example model:

experiments/WMT14/download.sh    # download WMT14 data into data/raw
experiments/WMT14/prepare.sh     # preprocess the data, and copy the files to experiments/WMT14/data
python3 -m translate experiments/WMT14/baseline.yaml --train -v   # train a baseline model on this data

Features

• YAML configuration files
• Beam-search decoder
• External language model
• Ensemble decoding
• Multiple encoders
• Hierarchical encoder
• Bidirectional encoder
• Local attention model
• Convolutional attention model
• Detailed logging
• Periodic BLEU evaluation
• Periodic checkpoints
• Multi-task training: train on several tasks at once (e.g. French->English and German->English MT)
• Subwords training and decoding
• Input binary features instead of text
• Pre-processing script: we provide a fully-featured Python script for data pre-processing (vocabulary creation, lowercasing, tokenizing, splitting, etc.)
• Dynamic RNNs: we use symbolic loops instead of statically unrolled RNNs. This means faster model creation, and that we don't need buckets

Speech translation

• To replicate the results from arxiv.org/abs/1612.01744, you should look at the branch speech. The default branch baseline implements a different model, from arxiv.org/abs/1409.0473.

Credits

• This project is based on TensorFlow's reference implementation
• We include some of the pre-processing scripts from Moses
• The scripts for subword units come from github.com/rsennrich/subword-nmt