Three heads are better than one
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Using language and translation models to select the best among outputs from multiple MT systems
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Multi-engine machine translation with voted language model
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Hierarchical Phrase-Based Translation
Computational Linguistics
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Collaborative decoding: partial hypothesis re-ranking using translation consensus between decoders
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
The feature subspace method for SMT system combination
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Hybrid decoding: decoding with partial hypotheses combination over multiple SMT systems
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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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The state-of-the-art system combination method for machine translation (MT) is the word-based combination using confusion networks. One of the crucial steps in confusion network decoding is the alignment of different hypotheses to each other when building a network. In this paper, we present new methods to improve alignment of hypotheses using word synonyms and a two-pass alignment strategy. We demonstrate that combination with the new alignment technique yields up to 2.9 BLEU point improvement over the best input system and up to 1.3 BLEU point improvement over a state-of-the-art combination method on two different language pairs.