A statistical approach to machine translation
Computational Linguistics
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
Phrase-Based Statistical Machine Translation
KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in 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
Fast decoding and optimal decoding for machine translation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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
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
N-gram-based Machine Translation
Computational Linguistics
Human interaction for high-quality machine translation
Communications of the ACM - A View of Parallel Computing
Manual and automatic evaluation of machine translation between European languages
StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
Inference of finite-state transducers from regular languages
Pattern Recognition
CLAGI '09 Proceedings of the EACL 2009 Workshop on Computational Linguistic Aspects of Grammatical Inference
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Statistical Machine Translation is receiving more and more attention every day due to the success that the phrase-based alignment models are obtaining. However, despite their power, state-of-the-art systems using these models present a series of disadvantages that lessen their effectiveness in working environments where temporal or spacial computational resources are limited. A finite-state framework represents an interesting alternative because it constitutes an efficient paradigm where quality and realtime factors are properly integrated in order to build translation devices that may be of help for their potential users. Here, we describe a way to use the bilingual information in a phrase-based model in order to implement a phrase-based ngram model using finite state transducers. It will be worth the trouble due to the notable decrease in computational requirements that finite state transducers present in practice with respect to the use of some well-known stack-decoding algorithms. Results for the French-English EuroParl benchmark corpus from the 2006 Workshop on Machine Translation of the ACL are reported.