Machine Translation

Architecture of a Statistical Machine Translation System

The goal of machine translation is the translation of a text given in some natural source language into a natural target language. The input can be either a written sentence or a spoken sentence that was recognized by a speech recognition system. At the Lehrstuhl für Informatik VI, we apply statistical methods similar to those in speech recognition. Stochastic models describe the structure of the sentences of the target language - the language model - and the dependencies between words of the source and the target language - the translation model (see figure). The translation model is decomposed into the lexicon model which determines the translations of the words in the source language and the alignment model forming a mapping between the words in the source language string and the words in the target language string. These models are trained automatically on a corpus of bilingual source/target sentence pairs. In this approach, it is not necessary to manually design rules for the translation or the construction of sentences. A search algorithm determines the target language sentence that has the highest probability given the source language sentence.
The statistical approach to machine translation is particularly suitable for the translation of spontaneous speech, where the translation approach has to cope with colloquial language and speech recognition errors.

  • Ongoing Research in Machine Translation