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Pytorch implementation of Neural Machine Translation using"Quasi-Recurrent Neural Networks", ICLR 2017

• NumPy >= 1.11.1
• Pytorch >= 0.2.0

• layer.py : Implementation of the quasi-recurrent layer
• model.py: Implementation of the Encoder-Decoder model using qrnn layer
• train.py: Code to train a NMT model
• decode.py: Code to translate a source file using a trained model

To train a quasi-rnn NMT model,

$pythontrain.py --kernel_size3 \                  --hidden_size640 \                  --emb_size500 \                  --num_enc_symbols30000 \                  --num_dec_symbols30000 ...

To run the trained model for translation,

$pythoneval.py  --model_path $path_to_model \                  --decode_input $path_to_source \                  --decode_output $path_to_output                  --max_decode_step300 \                  --batch_size30 ...

For simplicity, we used greedy decoding at each time step, not the beam search decoding.

For more in-depth exploration, QRNN API for Pytorch is available:https://github.com/salesforce/pytorch-qrnn

For any comments and feedbacks, please email me atpjh0308@gmail.com or open an issue here.