Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Lattice-based minimum error rate training for statistical machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Mixture-model adaptation for SMT
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Regularization and search for minimum error rate training
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Stabilizing minimum error rate training
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Hi-index | 0.00 |
NRC's Portage system participated in the English-French (E-F) and French-English (F-E) translation tasks of the ACL WMT 2010 evaluation. The most notable improvement over earlier versions of Portage is an efficient implementation of lattice MERT. While Portage has typically performed well in Chinese to English MT evaluations, most recently in the NIST09 evaluation, our participation in WMT 2010 revealed some interesting differences between Chinese-English and E-F/F-E translation, and alerted us to certain weak spots in our system. Most of this paper discusses the problems we found in our system and ways of fixing them. We learned several lessons that we think will be of general interest.