GREAT: a finite-state machine translation toolkit implementing a Grammatical Inference Approach for Transducer Inference (GIATI)
GREAT is a finite-state toolkit which is devoted to Machine Translation and that learns structured models from bilingual data. The training procedure is based on grammatical inference techniques to obtain stochastic transducers that model both the structure of the languages and the relationship between them. The inference of grammars from natural language causes the models to become larger when a less restrictive task is involved; even more if a bilingual modelling is being considered. GREAT has been successful to implement the GIATI learning methodology, using different scalability issues to be able to deal with corpora of high volume of data. This is reported with experiments on the EuroParl corpus, which is a state-of-theart task in Statistical Machine Translation.