The nature of statistical learning theory
The nature of statistical learning theory
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Automatic acquisition of hierarchical transduction models for machine translation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Generation that exploits corpus-based statistical knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Evaluation metrics for knowledge-based machine translation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
A new quantitative quality measure for machine translation systems
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Evaluation metrics for generation
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Regression for machine translation evaluation at the sentence level
Machine Translation
Validity of an Automatic Evaluation of Machine Translation Using a Word-Alignment-Based Classifier
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
A re-examination on features in regression based approach to automatic MT evaluation
HLT-SRWS '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Student Research Workshop
Choosing the right translation: a syntactically informed classification approach
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Detection of non-native sentences using machine-translated training data
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Mining sequential patterns and tree patterns to detect erroneous sentences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Ranking vs. regression in machine translation evaluation
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Sentence correction incorporating relative position and parse template language models
IEEE Transactions on Audio, Speech, and Language Processing
Empirical methods in natural language generation
Improvement of machine translation evaluation by simple linguistically motivated features
Journal of Computer Science and Technology - Special issue on natural language processing
Linguistic measures for automatic machine translation evaluation
Machine Translation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Regression and ranking based optimisation for sentence level machine translation evaluation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Corroborating text evaluation results with heterogeneous measures
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A machine learning-based evaluation method for machine translation
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
DCU-symantec submission for the WMT 2012 quality estimation task
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
Fusion of word and letter based metrics for automatic MT evaluation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present a machine learning approach to evaluating the well-formedness of output of a machine translation system, using classifiers that learn to distinguish human reference translations from machine translations. This approach can be used to evaluate an MT system, tracking improvements over time; to aid in the kind of failure analysis that can help guide system development; and to select among alternative output strings. The method presented is fully automated and independent of source language, target language and domain.