A framework of a mechanical translation between Japanese and English by analogy principle
Proc. of the international NATO symposium on Artificial and human intelligence
A statistical approach to machine translation
Computational Linguistics
A systematic comparison of various statistical alignment models
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
Comparing example-based and statistical machine translation
Natural Language Engineering
SSST '07 Proceedings of the NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation
OpenMaTrEx: a free/open-source marker-driven example-based machine translation system
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
Panning for EBMT gold, or "Remembering not to forget"
Machine Translation
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(Way and Gough, 2005) provide an in-depth comparison of their Example-Based Machine Translation (EBMT) system with a Statistical Machine Translation (SMT) system constructed from freely available tools. According to a wide variety of automatic evaluation metrics, they demonstrated that their EBMT system outperformed the SMT system by a factor of two to one. Nevertheless, they did not test their EBMT system against a phrase-based SMT system. Obtaining their training and test data for English--French, we carry out a number of experiments using the Pharaoh SMT Decoder. While better results are seen when Pharaoh is seeded with Giza++ word- and phrase-based data compared to EBMT sub-sentential alignments, in general better results are obtained when combinations of this 'hybrid' data is used to construct the translation and probability models. While for the most part the EBMT system of (Gough & Way, 2004b) outperforms any flavour of the phrase-based SMT systems constructed in our experiments, combining the data sets automatically induced by both Giza++ and their EBMT system leads to a hybrid system which improves on the EBMT system per se for French--English.