The present chapter reviews the development of a hybrid Machine Translation (MT) methodology, which is readily portable to new language pairs. This MT methodology (which has been developed within the PRESEMT project) is based on sampling mainly monolingual corpora, with very limited use of parallel corpora, thus supporting portability to new language pairs. In designing this methodology, no assumptions are made regarding the availability of extensive and expensive-to-create linguistic resources. In addition, the general-purpose NLP tools used can be chosen interchangeably. Thus PRESEMT circumvents the requirement for specialised resources and tools so as to further support the creation of MT systems for diverse language pairs.

In the current chapter, the proposed hybrid MT methodology is compared to established MT systems, both in terms of design concept and in terms of output quality. More specifically, the translation performance of the proposed methodology is evaluated against that of existing MT systems. The chapter summarises implementation decisions, using the Greek-to-English language pair as a test case. In addition, the detailed comparison of PRESEMT to other established MT systems provides insight on their relative advantages and disadvantages, focusing on specific translation tasks and addressing both translation quality as well as translation consistency and stability. Finally, directions are discussed for improving the performance of PRESEMT. This will allow PRESEMT to move beyond the original requirements for an MT system for gisting, towards a high-performing general-purpose MT system.