Abstract This chapter has two main aims: (i) to present the state-of-the-art in Machine Translation (MT), namely Phrase-Based Statistical MT, together with the major competing paradigms used in MT research and development today; and (ii) to provide an overview of the MT research carried out by my team here at DCU, characterised here in terms of ‘hybrid MT’. In addition, we provide our views on the directions that MT research might take in the near future, and conclude the chapter with lists of further reading for the interested reader.

It is widely recognized that one of the best ways to learn a foreign language is through spoken dialogue with a native speaker. However, this is not a practical method in the classroom due to the one-to-one student/teacher ratio it implies. A potential solution to this problem is to rely on computer spoken dialogue systems to role play a conversational partner. This paper describes several multilingual dialogue systems specifically designed to address this need. Students can engage in dialogue with the computer either over the telephone or through audio/typed input at a Web page. Several different domains are being developed, in which a student’s conversational interaction is assisted by a software agent functioning as a “tutor ” which can provide them with translation assistance at any time. Some of the research issues surrounding high-quality spoken language translation and dialogue modeling for language games are discussed. 1.

N66001-99-2-891804. Any opinions, findings, conclusions, or recommendations expressed in this material are those of