Neural Monkey

Neural Monkey is a universal toolkit for training neural models for sequence-to-sequence tasks. The system has been successfully tested on machine translation, multimodal machine translation, or automatic post-editing. It can be used, however, for many other tasks, including image captioning, part-of-speech tagging, sequence classification, etc.

Neural Monkey's primary goal is to allow for fast prototyping and easy extension, which makes it a toolkit-of-choice for researchers who want to implement and/or modify recently published techniques.

Neural Monkey is written in Python 3 and built on the TensorFlow library. It supports training on GPUs with a minimum required effort. 

If you want to start using Neural Monkey, clone it from its GitHub page. There is a bunch of tutorials and plenty of other useful information either in the package README, or in the documentation.

References

Neural Monkey was used in the following publications:

• Libovický, J., Helcl, J., Tlustý, M., Bojar, O., & Pecina, P. (2016, August). CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks. In Proceedings of the First Conference on Machine Translation (p. 646)
• Avramidis, E., Macketanz, V., Burchardt, A., Helcl, J., & Uszkoreit, H. (2016, October). Deeper Machine Translation and Evaluation for German. In 2nd Deep Machine Translation Workshop (p. 29).
• Bojar, O., Sudarikov, R., Kocmi, T., Helcl, J., & Cıfka, O. (2016). UFAL Submissions to the IWSLT 2016 MT Track. In Proceedings of the ninth International Workshop on Spoken Language Translation (IWSLT), Seattle, WA.