Hybrid Architectures for Machine Translation Systems

Although some progress has been made on the quality of Machine Translation in recent years, there is still a significant potential for quality improvement. There has also been a shift in paradigm of machine translation, from “classical” rule-based systems like METAL or LMT¹ towards example-based or statistical MT.² It seems to be time now to evaluate the progress and compare the results of these efforts, and draw conclusions for further improvements of MT quality.The paper starts with a comparison between statistical MT (henceforth: SMT) and rule-based MT (henceforth: RMT) systems, and describes the set-up and the evaluation results; the second section analyses the strengths and weaknesses of the respective approaches, and the third one discusses models of an architecture for a hybrid system.