Machine Translation: State of the Art, Trends and User Perspective.

Currently, no machine translation (MT) system is capable of successfully imitating the behavior of a human translator, and there exists no formal description of what an MT system is supposed to do. The biggest problem in practice is disambiguation. However, various types of existing systems do help reduce language barriers, even if they are poor imitations of the human translator. Users of such systems determine the utility of the systems. Three groups of users are distinguished: big companies and institutions; professional and occasional translators; and monolingual users. Big MT systems are becoming more cost-effective. Smaller systems providing high-quality output for restricted tasks and domains and multilingual systems based on spoken dialogue are being developed. Whereas market developments show a trend toward more specialized and restricted systems, the trend in research is to widen the scope of machine translation, moving from sentence to text, focusing on disambiguation based on domain restrictions, and using non-traditional statistical methods. Work is needed to provide lexicological and terminological resources for East European languages, and to create large collections of real-life data, including monolingual and bilingual text corpora. (MSE)