Current Machine Translation (MT) technology has serious limitations: there are, on the one hand, the rule-based systems which require handcrafted linguistic rules and their manual construction is time consuming and expensive, and, on the other hand, the statistical systems which try to learn how to translate by analyzing the translation patterns found in large collections of human translations and these systems are effective only when large amounts of parallel corpora are available.
parallel corpora are only available for a limited number of language pairs and domains.