Military Communications and Information Technology: A Trusted ...

Chapter 3: Information Technology for Interoperability and Decision...
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Figure 4. Excerpt of a Translation Model generated by the ISAF-MT SMT System
Different factors can influence the performance of an SMT system, e.g., the size
and quality of the training corpus (as described below), the integration of linguistic
knowledge or the different machine learning implementations. Training data are
highly essential for SMT performance. There are certain requirements that, the more
satisfied, can lead to better translation quality:
• Size of the training data: In general, the larger a training corpus is the more
expressions in terms of vocabulary and word combinations it will cover.
Therefore, corpus size is significant for the training of the system and system
performance will improve when more training data are provided.
• Translation quality of the training data: The translation quality of the training
corpus directly corresponds to the correctness of the translation model. This
means the more correct translations the training corpus contains the better
the system will be able to learn how to produce correct translations.
• Domain adaption of the training data: Language is highly ambiguous, i.e.,
a word, phrase or sentence can have different meanings and therefore
may also have different translations. The meaning of a text segment may
depend on its contextual domain. If a system has been trained for a specific
domain it will choose translations appropriate for that domain. The more
domain-specific a training corpus is, the better the translation performance
for texts from this domain will be.
Linguistic expertise can be applied to adapt SMT technology to the specific
language pair of interest, thus improving translation quality. Also, different SMT
approaches and machine learning algorithms can be applied to modify a system
in terms of translation quality as well as efficiency. As SMT is an active research
field, better and faster approaches are constantly being implemented.
The advantages that SMT holds over other machine translation approaches
are, for example, the same that statistical NLP technology in general holds over
rule-based approaches, i.e.: