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There are different machine translation approaches, from simple word-toword
translation, to rule-based approaches to statistical machine translation, and
hybrid approaches. Word-to-word translations operate on the basis of dictionaries.
Rule-based approaches apply a set of handwritten linguistic rules that describe how
to translate one specific source language into one specific target language. In contrast,
SMT is based on machine learning. Most modern MT research is performed
in statistical machine translation.
The objective of SMT is to find the most probable translation for a given
sentence. SMT uses machine learning techniques which means that the system
learns how to translate source language texts into target language texts, based on
training data and by the application of learning algorithms. The focus thereby is not
on generating a perfect word-to-word translation but on transferring the meaning
of the source language text into the target language.
Figure 3. Excerpt of the Dari-German Corpus
To build an SMT system, it has to be trained by the application of machine
learning techniques. The training data are parallel corpora, i.e., large collections
of texts in the source as well as in the target language that represent translations
of each other (see Figure 3 for an example). During the training stage, the system
statistically analyses the training corpus in order to learn a so-called translation
model. For this, different learning algorithms may be applied. The model assigns diverse
probabilities to co-occurrences of textual segments (e.g., phrases) in the source
language and in the target language. The model then contains phrases in the source
language together with possible translations in the target languages and different
probabilities for each specific phrase pair. Figure 4 shows an excerpt of a translation
model that has been generated by our ISAF-MT SMT system. The purpose
of the translation model is to generate a reasonable translation. In addition to
the translation model, a language model is trained using a monolingual text corpus
in the target language. The language model contains n-gram probabilities for word
sequences of the target language. Based on the language model the system is designed
to derive good target language expressions in terms of naturalness. After
training on the training corpus, the SMT system is able to translate new input text.
It will generate the translation of highest probability based on the trained models.