The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
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
The LRC machine translation system
Computational Linguistics - Special issue on machine translation
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
Statistical post-editing on SYSTRAN's rule-based translation system
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
TrustRank: inducing trust in automatic translations via ranking
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Translating by post-editing: is it the way forward?
Machine Translation
DEPFIX: a system for automatic correction of Czech MT outputs
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
Statistical error correction methods for domain-specific ASR systems
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
A new fuzzy rule-based classification system for word sense disambiguation
Intelligent Data Analysis
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Automatic post-editing (APE) systems aim at correcting the output of machine translation systems to produce better quality translations, i.e. produce translations can be manually post-edited with an increase in productivity. In this work, we present an APE system that uses statistical models to enhance a commercial rule-based machine translation (RBMT) system. In addition, a procedure for effortless human evaluation has been established. We have tested the APE system with two corpora of different complexity. For the Parliament corpus, we show that the APE system significantly complements and improves the RBMT system. Results for the Protocols corpus, although less conclusive, are promising as well. Finally, several possible sources of errors have been identified which will help develop future system enhancements.