WordNet: a lexical database for English
Communications of the ACM
Generalized algorithms for constructing statistical language models
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Machine translation system combination using ITG-based alignments
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Machine translation system combination with flexible word ordering
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Fluency, adequacy, or HTER?: exploring different human judgments with a tunable MT metric
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Joint optimization for machine translation system combination
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
OpenFst: a general and efficient weighted finite-state transducer library
CIAA'07 Proceedings of the 12th international conference on Implementation and application of automata
JHU system combination scheme for WMT 2010
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Findings of the 2011 Workshop on Statistical Machine Translation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Review of hypothesis alignment algorithms for MT system combination via confusion network decoding
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
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This paper describes the JHU system combination scheme used in WMT-11. The JHU system combination is based on confusion network alignment, and inherited the framework developed by (Karakos et al., 2008). We improved our core system combination algorithm by making use of TER-plus, which was originally designed for string alignment, for alignment of confusion networks. Experimental results on French-English, German-English, Czech-English and Spanish-English combination tasks show significant improvements on BLEU and TER by up to 2 points on average, compared to the best individual system output, and improvements compared with the results produced by ITG which we used in WMT-10.