Open Source Text Processing Project: Moses

Deep Learning Specialization on Coursera

Moses, the machine translation system

Project Website:

Github Link:


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). Once you have a trained model, an efficient search algorithm quickly finds the highest probability translation among the exponential number of choices.

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

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
The Experiment Management System makes using Moses much easier

Get started
The released software includes a command line executable which can used for decoding. The source code for the decoder, can be downloaded from github. Download the latest release or the current snapshot from github.
Learn about the decoder, training models, and tuning. Follow the step-by-step guide to build a baseline translation system. The documentation available at this web side is also compiled in a printable manual.


Open Source Text Processing Project: Moses — 1 Comment

  1. I appreciated you indeed more than what can I say but is better to include speech to text of cross-lingual system(English speech to portugess text).i.e.Integration of Machine Translation and ASR.

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