MMT is a context-aware, incremental and distributed general purpose Machine Translation technology.
MMT is:
- Simple to use, fast to train, and easy to scale with respect to domains, data, and users.
- Trained by pooling all available domains/projects/customers data and translation memories in one folder.
- Queried by providing the sentence to be translated and optionally some context text.
MMT's goal is to deliver the quality of multiple custom engines by adapting on the fly to the provided context.
You can find more information on: http://www.modernmt.eu
This release allows you to create an MT engine, from a collection of line aligned parallel data or TMX files, that can be queried via a REST API.
Intro video: http://87k.eu/lk9l
Read INSTALL.md
The distribution includes a small dataset (folder examples/data/train) to train and test translations from
English to Italian in three domains.
$ ./mmt create en it examples/data/train$ ./mmt startYou can stop it with the command stop.
Let's now use the command-line tool mmt to query the engine with the sentence hello world and context computer:
$ ./mmt translate --context computer "hello world"
ModernMT Translate command line
>> Context: ibm 87%, europarl 13%
>> hello mondo
Next, we are going to improve the partial translation hello mondo.
Note: You can query MMT directly via REST API, to learn more on how to do it, visit the Translate API page in the project Wiki.
Let's now add a contribution to te existing engine, without need for retraining, in order to improve the previous translation. We will use again the command-line tool mmt:
./mmt add ibm "hello Mike!" "ciao Mike!"
And now repeat the previous translation query: the engine has just learned a new word and the result is immediately visible.
$ ./mmt translate --context computer "hello world"
ModernMT Translate command line
>> Context: ibm 87%, europarl 13%
>> ciao mondo
How is your engine performing vs the commercial state-of-the-art technologies?
Should I use Google Translate or ModernMT given this data?
Evaluate helps you answer these questions.
Before training, MMT has removed sentences corresponding to 1% of the training set (or up to 1200 lines at most). During evaluate these sentences are used to compute the BLUE Score and Matecat Post-Editing Score against the MMT and Google Translate.
With your engine running, just type:
./mmt evaluate
The typical output will be
Testing on 980 sentences...
Matecat Post-Editing Score:
MMT : 75.10 (Winner)
Google Translate : 73.40 | API Limit Exeeded | Connection Error
BLEU:
MMT : 37.50 (Winner)
Google Translate : 36.10 | API Limit Exeeded | Connection Error
Translation Speed:
MMT : 1.75s per sentence
Google Translate : 0.76s per sentence
If you want to test on a different test-set just type:
./mmt evaluate --path path/to/your/test-set
Notes: To run Evaluate you need internet connection for Google Translate API and the Matecat Post-Editing Score API. MMT comes with a limited Google Translate API key.
Matecat kindly provides unlimited-fair-usage, access to their API to MMT users.
You can select your Google Translate API Key by typing:
./mmt evaluate --gt-key YOUR_GOOGLE_TRANSLATE_API_KEY
If you don't want to use Google Translate just type a random key.
Following this README you have learned the basic usage of ModernMT. Most users would be interested in creating their own engine with their own data, you can find more info in the Wiki Create an engine from scratch
ModernMT comes with built-in REST API that allows the user to control every single feature of MMT via a simple and powerful interface. You can find the API Documentation in the ModernMT Wiki.
You can setup a cluster of MMT nodes in order to load balancing translation requests. In fact also tuning and evaluation can be drastically speed-up if runned on an MMT cluster.
You can learn more on the Wiki page MMT Cluster.