hieuhoang / amun

Stand-alone C++ decoder for RNN-based NMT models. Can decode with default models from Marian and Nematus.

Amun

Stand-alone C++ decoder for RNN-based NMT models. Can decode with default models from Marian and Nematus.

This tool was a part of Marian, which now has its own decoder. Therefore, amun has been moved to a separate repository. For RNN models this decoder can still be faster on the GPU than the official Marian decoder.

If you use this, please cite:

Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, Alexandra Birch (2018). Marian: Fast Neural Machine Translation in C++

@InProceedings{mariannmt,
    title     = {Marian: Fast Neural Machine Translation in {C++}},
    author    = {Junczys-Dowmunt, Marcin and Grundkiewicz, Roman and
                 Dwojak, Tomasz and Hoang, Hieu and Heafield, Kenneth and
                 Neckermann, Tom and Seide, Frank and Germann, Ulrich and
                 Fikri Aji, Alham and Bogoychev, Nikolay and
                 Martins, Andr\'{e} F. T. and Birch, Alexandra},
    booktitle = {Proceedings of ACL 2018, System Demonstrations},
    pages     = {116--121},
    publisher = {Association for Computational Linguistics},
    year      = {2018},
    month     = {July},
    address   = {Melbourne, Australia},
    url       = {http://www.aclweb.org/anthology/P18-4020}
}

other papers about Amun:

@article{DBLP:journals/corr/Junczys-Dowmunt16c,
  author    = {Marcin Junczys{-}Dowmunt and
               Tomasz Dwojak and
               Hieu Hoang},
  title     = {Is Neural Machine Translation Ready for Deployment? {A} Case Study
               on 30 Translation Directions},
  journal   = {CoRR},
  volume    = {abs/1610.01108},
  year      = {2016},
  url       = {http://arxiv.org/abs/1610.01108},
  archivePrefix = {arXiv},
  eprint    = {1610.01108},
  timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/Junczys-Dowmunt16c},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{W18-2714,
  author = 	"Hoang, Hieu
		and Dwojak, Tomasz
		and Krislauks, Rihards
		and Torregrosa, Daniel
		and Heafield, Kenneth",
  title = 	"Fast Neural Machine Translation Implementation",
  booktitle = 	"Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"116--121",
  location = 	"Melbourne, Australia",
  url = 	"http://aclweb.org/anthology/W18-2714"
}

Website

More information on https://marian-nmt.github.io

• Quick start
• Installation and usage documentation
• Usage examples

Acknowledgements

The development of Marian received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreements 688139 (SUMMA; 2016-2019), 645487 (Modern MT; 2015-2017), 644333 (TraMOOC; 2015-2017), 644402 (HiML; 2015-2017), the Amazon Academic Research Awards program, the World Intellectual Property Organization, and is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract #FA8650-17-C-9117.

This software contains source code provided by NVIDIA Corporation.