Moses for Mere Mortals
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Moses for Mere Mortals (MMM)[1] is a free open source software composed of a set of scripts designed to allow the automation of processes for the installation and operation of the Moses Open Source Translation System, a statistical machine translation system.
MMM builds a translation chain prototype with Moses + IRSTLM + RandLM + MGIZA.[2][3]
The first version of Moses for Mere Mortals was published in November 2009, and it has been updated and tested on Linux - Ubuntu distributions. MMM is available in the GitHub Project Hosting website.[1]
Its main aims are to:
- help build a prototype of a translation chain for the real world;
- guide the first steps of users that are just beginning to use Moses;
- enable a simple and quick evaluation of Moses;
- enable the user to do his/her own translations without having to trust third (translating) parties;
- integrate machine translation and translation memories.
Even though the main thrust is centred on Linux, two Windows add-ins help to make the bridge from Windows to Linux and then back from Linux.
General features
Moses allows the training of corpora where every word is presented together with, for instance, its respective lemma and/or part of speech tag (“factored training”). The scripts do not cover this type of training.
MMM consists of seven scripts for Linux, thoroughly tested with Ubuntu (12.04 and 14.04, 64-bit):
- Install: To install in Ubuntu the packages on which both Moses and Moses for Mere Mortals depend.
- Create: To compile Moses and the other required packages with a single command.
- Make-test-files: To extract from the original corpus a corpus for training, files for tuning and files for testing the training results.
- Train: To train the language pairs needed, as Moses is language-independent and can work with any language/alphabet.
- Translate: To produce machine translations of new documents.
- Score: To automatically evaluate Moses translations against a human translation taken as a gold standard, using BLEU and NIST metrics algorithms, in order to have an idea of the level of performance.
- Transfer training-to-another-location: To transfer engines/trainings to other folders in the same computer or to a different computer.
MMM comes with a 200,000-segment demonstration corpus — which is too small to do justice to the qualitative results achievable with Moses, but capable of giving a realistic view of the relative duration of the steps involved and useful to test whether the installation was correctly done. In order to get good results, one generally needs a corpus with several million segments. Each orthogonal corpus consists of two strictly aligned UTF-8 files, one in the source language and the other in the target language. No grammar knowledge is required, though some language pairs give better results than others. In a general way, morphologically rich languages give worse results.