Machine Translation

 
 
 
 
 
 
 
 
Technicolor
Machine Translation
 

Automatic translation of text breaks the language barrier: It allows instant access to information in foreign languages.

Target users and customers

  • Researchers
  • Developers
  • Integrators

Application sectors

As translation quality is far below the work of professional human translators, machine translation is targeted to situations where instant access and low cost are key and high quality is not demanded, for example:

  • Internet search (cross-language document retrieval)
  • Internet (on-the-fly translation of foreign-language websites or news feeds)

Description:

Machine translation is a very hard problem in computer science and has been worked on for decades. The corpus-based methods that emerged in the 1990’s allow the computer to actually learn translation from existing bilingual texts – you could say, from many translation examples.

A correct mapping is indeed not easy to learn, as the translation of a word depends on its context, and word orders typically differ across languages. It is fascinating to see this technology improving over the years. The learning methods are more of a mathematical kind and can be applied to any language pair.

Technical requirements:

Translation is a memory-intense process, so the typical set-up is to have one or several computers in the internet serving the translation requirements of many users.

Conditions for access and use:

RWTH provides open-source translation tools free of charge for academic usage. Other usage should be subject to a bilateral agreement.

Bildschirmfoto vom 2013-07-24 163A323A20

Partners:

  • RWTH Aachen

Contact details:

Volker Steinbiss
steinbiss@informatik.rwth-aachen.de

RWTH Aachen University
Lehrstuhl für Informatik 6
Templergraben 55
52072 Aachen

http://www-i6.informatik.rwth-aachen.de