Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Towards a unified approach to memory- and statistical-based machine translation
ACL '01 Proceedings of the 39th 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
Efficient Computation of Gapped Substring Kernels on Large Alphabets
The Journal of Machine Learning Research
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Word sense disambiguation using label propagation based semi-supervised learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for 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
Kernel regression based machine translation
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Efficient graph-based semi-supervised learning of structured tagging models
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Semi-Supervised Learning with Measure Propagation
The Journal of Machine Learning Research
Unsupervised semantic role induction with graph partitioning
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning translation consensus with structured label propagation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
The trouble with SMT consistency
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
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Current phrase-based statistical machine translation systems process each test sentence in isolation and do not enforce global consistency constraints, even though the test data is often internally consistent with respect to topic or style. We propose a new consistency model for machine translation in the form of a graph-based semi-supervised learning algorithm that exploits similarities between training and test data and also similarities between different test sentences. The algorithm learns a regression function jointly over training and test data and uses the resulting scores to rerank translation hypotheses. Evaluation on two travel expression translation tasks demonstrates improvements of up to 2.6 BLEU points absolute and 2.8% in PER.