Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
A systematic comparison of various statistical alignment models
Computational Linguistics
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Greedy decoding for statistical machine translation in almost linear time
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Shared task: statistical machine translation between European languages
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Value elimination: bayesian inference via backtracking search
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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We introduce a novel framework for the expression, rapid-prototyping, and evaluation of statistical machine-translation (MT) systems using graphical models. The framework extends dynamic Bayesian networks with multiple connected different-length streams, switching variable existence and dependence mechanisms, and constraint factors. We have implemented a new general-purpose MT training/decoding system in this framework, and have tested this on a variety of existing MT models (including the 4 IBM models), and some novel ones as well, all using Europarl as a test corpus. We describe the semantics of our representation, and present preliminary evaluations, showing that it is possible to prototype novel MT ideas in a short amount of time.