Variational Neural Machine Translation System

Implemented by Pytorch 0.4, some modules references to OpenNMT-py.

References

1. Su, Jinsong, et al. "Variational Recurrent Neural Machine Translation." arXiv preprint arXiv:1801.05119 (2018).

2. Zhang, Biao, et al. "Variational neural machine translation." arXiv preprint arXiv:1605.07869 (2016)

Differences

For Variational NMT, I did not use the mean-pooling for both sides (source and target). I tested only using the last source hidden state is sufficient to achieve good performance.

For Variational Recurrent NMT, I tested only using the current RNN state is sufficient to achieve good performance.

The paper

Yang, Zichao, et al. "Improved variational autoencoders for text modeling using dilated convolutions." arXiv preprint arXiv:1702.08139 (2017).

explains the reason why use GRU instead of LSTM for building RNN cell, in general, VAE-LSTM-decoder performs worse than vanilla-LSTM-decoder.

Usage

Training

    python train.py --config config/nmt.ini

Test

    python translate.py --config config/nmt.ini