Phrase-Based Statistical Machine Translation
KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Statistical approaches to computer-assisted translation
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
(Meta-) evaluation of machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Online learning for interactive statistical machine translation
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Log-linear weight optimisation via Bayesian adaptation in statistical machine translation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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New variations on the application of the passive-aggressive algorithm to statistical machine translation are developed and compared to previously existing approaches. In online adaptation, the system needs to adapt to real-world changing scenarios, where training and tuning only take place when the system is set-up for the first time. Post-edit information, as described by a given quality measure, is used as valuable feedback within the passive-aggressive framework, adapting the statistical models on-line. First, by modifying the translation model parameters, and alternatively, by adapting the scaling factors present in state-of-the-art SMT systems. Experimental results show improvements in translation quality by allowing the system to learn on a sentence-by-sentence basis.