Welcome to Moses!

Moses is a statistical machine translation system that allows you to automatically train translation models for any language pair. All you need is a collection of translated texts (parallel corpus). An efficient search algorithm finds quickly the highest probability translation among the exponential number of choices.

Features

• Moses allows the decoding of confusion networks and word lattices, enabling easy integration with ambiguous upstream tools, such as automatic speech recognizers or morphological analyzers
• Moses offers two types of translation models: phrase-based and tree-based
• Moses features factored translation models, which enable the integration linguistic and other information at the word level

Get started

The released software includes a command line executable which can used for decoding. The source code for the decoder, as well as binaries for Windows and Linux, can be downloaded from Sourceforge. Download the latest binary release. However, if you want the addition scripts to create factored phrase tables and train the weights of the models, please download the complete snapshot via SVN. This repository also contains regression tests, should you be interested in enhancing the decoder.

Learn about the decoder, training models, and tuning. Follow the step-by-step guide. The documentation available at this web side is also compiled in a printable manual.

Acknowledgement

The development of Moses is mainly supported under the EuroMatrix and EuroMatrixPlus projects, funded by the European Commission under Framework Programme 6 and 7, and received additional support from

• University of Edinburgh, Scotland
• RWTH Aachen, Germany
• Fondazione Bruno Kessler, Trento, Italy
• University of Maryland, College Park, United States
• Massachusetts Institute of Technology, United States
• Charles University, Prague, Czech Republic
• US funding agencies DARPA, NSF, and Department of Defence
• EU funding through the TC-Star project

Open Source License

Moses is licensed under the LGPL.