BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Confidence estimation for machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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
Semantic roles for SMT: a hybrid two-pass model
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Proceedings of the Second Workshop on Statistical Machine Translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Proceedings of the Third Workshop on Statistical Machine Translation
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Findings of the 2009 workshop on statistical machine translation
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Semantic role labeling using complete syntactic analysis
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Linguistic measures for automatic machine translation evaluation
Machine Translation
Linguistic features for quality estimation
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
Investigating the contribution of linguistic information to quality estimation
Machine Translation
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We present a model for the inclusion of semantic role annotations in the framework of confidence estimation for machine translation. The model has several interesting properties, most notably: 1) it only requires a linguistic processor on the (generally well-formed) source side of the translation; 2) it does not directly rely on properties of the translation model (hence, it can be applied beyond phrase-based systems). These features make it potentially appealing for system ranking, translation re-ranking and user feedback evaluation. Preliminary experiments in pairwise hypothesis ranking on five confidence estimation benchmarks show that the model has the potential to capture salient aspects of translation quality.