Statistical methods for speech recognition
Statistical methods for speech recognition
Inference of Finite-State Transducers by Using Regular Grammars and Morphisms
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Translation with Inferred Stochastic Finite-State Transducers
Computational Linguistics
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Inference of finite-state transducers from regular languages
Pattern Recognition
Learning finite state transducers using bilingual phrases
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
GREAT: open source software for statistical machine translation
Machine Translation
Stochastic K-TSS bi-languages for machine translation
FSMNLP '11 Proceedings of the 9th International Workshop on Finite State Methods and Natural Language Processing
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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-the-art task in Statistical Machine Translation.