The EuTrans Spoken Language Translation System
Machine Translation
Defense of the ansatz for dynamical hierarchies
Artificial Life
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improve the Learning of Subsequential Transducers by Using Alignments and Dictionaries
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
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
A systematic comparison of various statistical alignment models
Computational Linguistics
Stochastic Finite-State Models for Spoken Language MachineTranslation
Machine Translation
Learning dependency translation models as collections of finite-state head transducers
Computational Linguistics - Special issue on finite-state methods in NLP
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Probabilistic Finite-State Machines-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Translation with cascaded finite state transducers
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
The Alignment Template Approach to Statistical Machine Translation
Computational Linguistics
Machine Translation with Inferred Stochastic Finite-State Transducers
Computational Linguistics
A weighted finite state transducer translation template model for statistical machine translation
Natural Language Engineering
Learning finite-state models for machine translation
Machine Learning
Statistical approaches to computer-assisted translation
Computational Linguistics
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Inference of finite-state transducers from regular languages
Pattern Recognition
GREAT: open source software for statistical machine translation
Machine Translation
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The inference of finite-state transducers from bilingual training data plays an important role in many natural-language tasks and mainly in machine translation. However, there are only a few techniques to infer such models. One of these techniques is the grammatical inference and alignments for transducer inference (GIATI) technique that has proven to be very adequate for speech translation, text-input machine translation, or computer-assisted translation. GIATI is a heuristic technique that requires segmented training data (i.e., the input sentences and the output sentences must be segmented with the restriction that the input segments and the output segments must be monotone aligned). For the purpose of obtaining segmented training data, pure statistical word-alignment models are used. This technique is revisited in this article. The main goal is to formally derive the complete GIATI technique using classical expectation-maximization statistical estimation procedure. This new approach allows us to avoid a hard dependence on heuristic "external" statistical techniques (statistical alignments and n-grams). A first set of experimental results obtained in a machine-translation task are also reported to initially validate this new version of the inference technique of finite-state transducers.