Moses (machine translation)
 | This article includes a list of general references, but it lacks sufficient corresponding inline citations. (October 2016) |
| Developer(s) | University of Edinburgh |
|---|---|
| Stable release | 4.0[1]
/ October 5, 2017 |
| Repository | |
| Written in | C++, Perl |
| Operating system | Windows, Linux, macOS |
| Type | Machine translation |
| License | LGPL |
| Website | statmt |
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
[edit]
References
[edit]- ^ "Moses - Releases". Statmt.org. Retrieved 2016-10-22.
- ^ "Moses: Bringing machine translation to the masses".
- ^ "Moses". SlideShare. 2013-11-28. Retrieved 2024-07-16.
Further reading
[edit]- 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
[edit]
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