1. See also

2. References

3. Further reading

4. External links

Moses is a free software, statistical machine translation engine that can be used to train statistical models of text translation from a source language to a target language. Moses then allows new source-language text to be decoded using these models to produce automatic translations in the target language. Training requires a parallel corpus of passages in the two languages, typically manually translated sentence pairs. Moses is released under the LGPL licence and available both as source code and binaries for Windows{{Citation needed|date=October 2016}} and Linux. Its development is primarily supported by the EuroMatrix project, with funding by the European Commission.

Among its features are:

• A beam search algorithm that quickly finds the highest probability translation within a number of choices
• Phrase-based translation of short text chunks
• Handles words with multiple factored representations to enable the integration of linguistic and other information (e.g., surface form, lemma and morphology, part-of-speech, word class)
• Decodes ambiguous forms of a source sentence, represented as a confusion network, to support integration with upstream tools such as speech recognizers
• Support for large language models (LMs) such as IRSTLM (an exact LM using memory-mapping) and RandLM (an inexact LM based on Bloom filters)

See also

• Apertium
• OpenLogos
• Comparison of machine translation applications
• Machine translation

References

Further reading

• Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, Evan Herbst. (2007) "Moses: Open Source Toolkit for Statistical Machine Translation". Annual Meeting of the Association for Computational Linguistics (ACL), demonstration session, Prague, Czech Republic, June 2007.

External links

• {{Official website|statmt.org/moses/}}
• IRSTLM Homepage
• RandLM Homepage

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