Machine Translation is as old as the field of Computational Linguistics itself.
It is also a problem that has been predicted to „be solved in the next five
years“ many times, but in fact it is not yet solved today. Machine
has been naively considered a simple problem solvable by simple statistical
means, then studied in depth by complicated but unsuccessful detailed sets of
rules trying to describe all details of natural language use, only to return to
statistical approach on a completely different level, using a combination of
linguistic analysis and powerful machine learning algorithms.
In the lecture, the basic statistical approach based on information theory will
be described followed by the description of today’s phrase-based statistical
state-of-the-art systems, including new approaches using deep language analysis.