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Moses (machine translation)

Topic: Software

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Moses
Developer(s)University of Edinburgh
Stable release
4.0[1] / October 5, 2017; 8 years ago (2017-10-05)
Written inC++, Perl
Operating systemWindows, Linux, macOS
TypeMachine translation
LicenseLGPL
Websitestatmt.org/moses

Moses is a statistical machine translation engine that can be used to train statistical models of text translation from a source language to a target language, developed by the University of Edinburgh.[2] 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 free and open-source software, released under the GNU Library Public License (LGPL), and available as source code and binary files for Windows[3] and Linux. Its development is supported mainly 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 set of choices
  • Phrase-based translation of short text chunks
  • Handles words with multiple factored representations to enable integrating 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 integrating 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

References

  1. ↑ "Moses - Releases". http://www.statmt.org/moses/?n=Moses.Releases. 
  2. ↑ "Moses: Bringing machine translation to the masses". https://www.ed.ac.uk/research/impact/science-engineering/moses. 
  3. ↑ "Moses" (in en). 2013-11-28. https://www.slideshare.net/slideshow/moses-28697208/28697208. 

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.




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