This package contains the Sockeye project, a sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet (Incubating). It implements state-of-the-art encoder-decoder architectures, such as:
- Deep Recurrent Neural Networks with Attention [Bahdanau, '14]
- Transformer Models with self-attention [Vaswani et al, '17]
- Fully convolutional sequence-to-sequence models [Gehring et al, '17]
If you have any questions or discover problems, please file an issue. You can also send questions to sockeye-dev-at-amazon-dot-com.
For technical information about Sockeye, see our paper on the arXiv (BibTeX):
Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton and Matt Post. 2017. Sockeye: A Toolkit for Neural Machine Translation. ArXiv e-prints.