@inproceedings{kreutzer-etal-2019-joey,
title = "Joey {NMT}: A Minimalist {NMT} Toolkit for Novices",
author = "Kreutzer, Julia and
Bastings, Jasmijn and
Riezler, Stefan",
editor = "Pad{\'o}, Sebastian and
Huang, Ruihong",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-3019",
doi = "10.18653/v1/D19-3019",
pages = "109--114",
abstract = "We present Joey NMT, a minimalist neural machine translation toolkit based on PyTorch that is specifically designed for novices. Joey NMT provides many popular NMT features in a small and simple code base, so that novices can easily and quickly learn to use it and adapt it to their needs. Despite its focus on simplicity, Joey NMT supports classic architectures (RNNs, transformers), fast beam search, weight tying, and more, and achieves performance comparable to more complex toolkits on standard benchmarks. We evaluate the accessibility of our toolkit in a user study where novices with general knowledge about Pytorch and NMT and experts work through a self-contained Joey NMT tutorial, showing that novices perform almost as well as experts in a subsequent code quiz. Joey NMT is available at \url{https://github.com/joeynmt/joeynmt}.",
}
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%0 Conference Proceedings
%T Joey NMT: A Minimalist NMT Toolkit for Novices
%A Kreutzer, Julia
%A Bastings, Jasmijn
%A Riezler, Stefan
%Y Padó, Sebastian
%Y Huang, Ruihong
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F kreutzer-etal-2019-joey
%X We present Joey NMT, a minimalist neural machine translation toolkit based on PyTorch that is specifically designed for novices. Joey NMT provides many popular NMT features in a small and simple code base, so that novices can easily and quickly learn to use it and adapt it to their needs. Despite its focus on simplicity, Joey NMT supports classic architectures (RNNs, transformers), fast beam search, weight tying, and more, and achieves performance comparable to more complex toolkits on standard benchmarks. We evaluate the accessibility of our toolkit in a user study where novices with general knowledge about Pytorch and NMT and experts work through a self-contained Joey NMT tutorial, showing that novices perform almost as well as experts in a subsequent code quiz. Joey NMT is available at https://github.com/joeynmt/joeynmt.
%R 10.18653/v1/D19-3019
%U https://aclanthology.org/D19-3019
%U https://doi.org/10.18653/v1/D19-3019
%P 109-114
Markdown (Informal)
[Joey NMT: A Minimalist NMT Toolkit for Novices](https://aclanthology.org/D19-3019) (Kreutzer et al., EMNLP-IJCNLP 2019)
ACL
- Julia Kreutzer, Jasmijn Bastings, and Stefan Riezler. 2019. Joey NMT: A Minimalist NMT Toolkit for Novices. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 109–114, Hong Kong, China. Association for Computational Linguistics.