sockeye 2.1.7

Sequence-to-Sequence framework for Neural Machine Translation

Sockeye

This package contains the Sockeye project, an open-source sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet (Incubating). Sockeye powers several Machine Translation use cases, including Amazon Translate. The framework implements state-of-the-art machine translation models with Transformers (Vaswani et al, 2017). Recent developments and changes are tracked in our CHANGELOG.

If you have any questions or discover problems, please file an issue. You can also send questions to sockeye-dev-at-amazon-dot-com.

Version 2.0

With version 2.0, we have updated the usage of MXNet by moving to the Gluon API and adding support for several state-of-the-art features such as distributed training, low-precision training and decoding, as well as easier debugging of neural network architectures. In the context of this rewrite, we also trimmed down the large feature set of version 1.18.x to concentrate on the most important types of models and features, to provide a maintainable framework that is suitable for fast prototyping, research, and production. We welcome Pull Requests if you would like to help with adding back features when needed.

Installation

The easiest way to run Sockeye is with Docker or nvidia-docker. To build a Sockeye image with all features enabled, run the build script:

python3 sockeye_contrib/docker/build.py

See the Dockerfile documentation for more information.

Documentation

For information on how to use Sockeye, please visit our documentation.

• For a quickstart guide to training a large data WMT model, see the WMT 2018 German-English tutorial.
• Developers may be interested in our developer guidelines.

Citation

For more information about Sockeye 2, see our paper (BibTeX):

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar. 2020. Sockeye 2: A Toolkit for Neural Machine Translation. To appear in EAMT 2020, project track.

For technical information about Sockeye 1, 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.

Research with Sockeye

Sockeye has been used for both academic and industrial research. A list of known publications that use Sockeye is shown below. If you know more, please let us know or submit a pull request (last updated: April 2020).

2020

• Dinu, Georgiana, Prashant Mathur, Marcello Federico, Stanislas Lauly, Yaser Al-Onaizan. "Joint translation and unit conversion for end-to-end localization." arXiv preprint arXiv:2004.05219 (2020)
• Hisamoto, Sorami, Matt Post, Kevin Duh. "Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?" Transactions of the Association for Computational Linguistics, Volume 8 (2020)
• Naradowsky, Jason, Xuan Zhan, Kevin Duh. "Machine Translation System Selection from Bandit Feedback." arXiv preprint arXiv:2002.09646 (2020)
• Niu, Xing, Marine Carpuat. "Controlling Neural Machine Translation Formality with Synthetic Supervision." Proceedings of AAAI (2020)

2019

• Agrawal, Sweta, Marine Carpuat. "Controlling Text Complexity in Neural Machine Translation." Proceedings of EMNLP (2019)
• Beck, Daniel, Trevor Cohn, Gholamreza Haffari. "Neural Speech Translation using Lattice Transformations and Graph Networks." Proceedings of TextGraphs-13 (EMNLP 2019)
• Currey, Anna, Kenneth Heafield. "Zero-Resource Neural Machine Translation with Monolingual Pivot Data." Proceedings of EMNLP (2019)
• Gupta, Prabhakar, Mayank Sharma. "Unsupervised Translation Quality Estimation for Digital Entertainment Content Subtitles." IEEE International Journal of Semantic Computing (2019)
• Hu, J. Edward, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, and Benjamin Van Durme. "Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting." Proceedings of NAACL-HLT (2019)
• Rosendahl, Jan, Christian Herold, Yunsu Kim, Miguel Graça,Weiyue Wang, Parnia Bahar, Yingbo Gao and Hermann Ney “The RWTH Aachen University Machine Translation Systems for WMT 2019” Proceedings of the 4th WMT: Research Papers (2019)
• Thompson, Brian, Jeremy Gwinnup, Huda Khayrallah, Kevin Duh, and Philipp Koehn. "Overcoming catastrophic forgetting during domain adaptation of neural machine translation." Proceedings of NAACL-HLT 2019 (2019)
• Tättar, Andre, Elizaveta Korotkova, Mark Fishel “University of Tartu’s Multilingual Multi-domain WMT19 News Translation Shared Task Submission” Proceedings of 4th WMT: Research Papers (2019)

2018

• Domhan, Tobias. "How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures". Proceedings of 56th ACL (2018)
• Kim, Yunsu, Yingbo Gao, and Hermann Ney. "Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies." arXiv preprint arXiv:1905.05475 (2019)
• Korotkova, Elizaveta, Maksym Del, and Mark Fishel. "Monolingual and Cross-lingual Zero-shot Style Transfer." arXiv preprint arXiv:1808.00179 (2018)
• Niu, Xing, Michael Denkowski, and Marine Carpuat. "Bi-directional neural machine translation with synthetic parallel data." arXiv preprint arXiv:1805.11213 (2018)
• Niu, Xing, Sudha Rao, and Marine Carpuat. "Multi-Task Neural Models for Translating Between Styles Within and Across Languages." COLING (2018)
• Post, Matt and David Vilar. "Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation." Proceedings of NAACL-HLT (2018)
• Schamper, Julian, Jan Rosendahl, Parnia Bahar, Yunsu Kim, Arne Nix, and Hermann Ney. "The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018." Proceedings of the 3rd WMT: Shared Task Papers (2018)
• Schulz, Philip, Wilker Aziz, and Trevor Cohn. "A stochastic decoder for neural machine translation." arXiv preprint arXiv:1805.10844 (2018)
• Tamer, Alkouli, Gabriel Bretschner, and Hermann Ney. "On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translation." Proceedings of the 3rd WMT: Research Papers (2018)
• Tang, Gongbo, Rico Sennrich, and Joakim Nivre. "An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation." Proceedings of 3rd WMT: Research Papers (2018)
• Thompson, Brian, Huda Khayrallah, Antonios Anastasopoulos, Arya McCarthy, Kevin Duh, Rebecca Marvin, Paul McNamee, Jeremy Gwinnup, Tim Anderson, and Philipp Koehn. "Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation." arXiv preprint arXiv:1809.05218 (2018)
• Vilar, David. "Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models." Proceedings of NAACL-HLT (2018)
• Vyas, Yogarshi, Xing Niu and Marine Carpuat “Identifying Semantic Divergences in Parallel Text without Annotations”. Proceedings of NAACL-HLT (2018)
• Wang, Weiyue, Derui Zhu, Tamer Alkhouli, Zixuan Gan, and Hermann Ney. "Neural Hidden Markov Model for Machine Translation". Proceedings of 56th ACL (2018)
• Zhang, Xuan, Gaurav Kumar, Huda Khayrallah, Kenton Murray, Jeremy Gwinnup, Marianna J Martindale, Paul McNamee, Kevin Duh, and Marine Carpuat. "An Empirical Exploration of Curriculum Learning for Neural Machine Translation." arXiv preprint arXiv:1811.00739 (2018)

2017

• Domhan, Tobias and Felix Hieber. "Using target-side monolingual data for neural machine translation through multi-task learning." Proceedings of EMNLP (2017).