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
Improving statistical natural language translation with categories and rules
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
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
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
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Confidence estimation for machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Word-level confidence estimation for machine translation using phrase-based translation models
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
HLT '02 Proceedings of the second international conference on Human Language Technology Research
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
Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Active learning for statistical phrase-based machine translation
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Detecting Macro-patterns in the European Mediasphere
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Assessing phrase-based translation models with oracle decoding
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Panning for EBMT gold, or "Remembering not to forget"
Machine Translation
Learning to translate: a statistical and computational analysis
Advances in Artificial Intelligence
Computing lattice BLEU oracle scores for machine translation
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Prediction of learning curves in machine translation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Oracle decoding as a new way to analyze phrase-based machine translation
Machine Translation
Lattice BLEU oracles in machine translation
ACM Transactions on Speech and Language Processing (TSLP)
An intelligent Web agent that autonomously learns how to translate
Web Intelligence and Agent Systems
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We present an extensive experimental study of a Statistical Machine Translation system, Moses (Koehn et al., 2007), from the point of view of its learning capabilities. Very accurate learning curves are obtained, by using high-performance computing, and extrapolations are provided of the projected performance of the system under different conditions. We provide a discussion of learning curves, and we suggest that: 1) the representation power of the system is not currently a limitation to its performance, 2) the inference of its models from finite sets of i.i.d. data is responsible for current performance limitations, 3) it is unlikely that increasing dataset sizes will result in significant improvements (at least in traditional i.i.d. setting), 4) it is unlikely that novel statistical estimation methods will result in significant improvements. The current performance wall is mostly a consequence of Zipf's law, and this should be taken into account when designing a statistical machine translation system. A few possible research directions are discussed as a result of this investigation, most notably the integration of linguistic rules into the model inference phase, and the development of active learning procedures.