acl acl2011 acl2011-155 acl2011-155-reference knowledge-graph by maker-knowledge-mining
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Author: Nan Duan ; Mu Li ; Ming Zhou
Abstract: This paper presents hypothesis mixture decoding (HM decoding), a new decoding scheme that performs translation reconstruction using hypotheses generated by multiple translation systems. HM decoding involves two decoding stages: first, each component system decodes independently, with the explored search space kept for use in the next step; second, a new search space is constructed by composing existing hypotheses produced by all component systems using a set of rules provided by the HM decoder itself, and a new set of model independent features are used to seek the final best translation from this new search space. Few assumptions are made by our approach about the underlying component systems, enabling us to leverage SMT models based on arbitrary paradigms. We compare our approach with several related techniques, and demonstrate significant BLEU improvements in large-scale Chinese-to-English translation tasks.
David Chiang. 2005. A Hierarchical Phrase-based Model for Statistical Machine Translation. In Proceedings of the Association for Computational Lin- guistics, pages 263-270. David Chiang. 2010. Learning to Translate with Source and Target Syntax. In Proceedings of the Association for Computational Linguistics, pages 1443-1452. Lei Cui, Dongdong Zhang, Mu Li, Ming Zhou, and Tiejun Zhao. 2010. Hybrid Decoding: Decoding with Partial Hypotheses Combination over Multiple SMT Systems. In Proceedings of the International Conference on Computational Linguistics, pages 2 14-222. John DeNero, David Chiang, and Kevin Knight. 2009. Fast Consensus Decoding over Translation Forests. In Proceedings of the Association for Computational Linguistics, pages 567-575. John DeNero, Franz Och. Translation. Association 975-983. Shankar Kumar, Ciprian Chelba and 2010. Model Combination for Machine In Proceedings of the North American for Computational Linguistics, pages Nan Duan, Mu Li, Dongdong Zhang, and Ming Zhou. 2010. Mixture Model-based Minimum Bayes Risk Decoding using Multiple Machine Translation Systems. In Proceedings of the International Conference on Computational Linguistics, pages 3 13-32 1. Michel Galley, Jonathan Graehl, Kevin Knight, Daniel Marcu, Steve DeNeefe, Wei Wang, and Ignacio Thayer. 2006. Scalable Inference and Training of Context-Rich Syntactic Translation Models. In Proceedings of the Association for Computational Linguistics, pages 961-968. Xiaodong He, Mei Yang, Jianfeng Gao, Patrick Nguyen, and Robert Moore. 2008. Indirect-HMMbased Hypothesis Alignment for Combining Outputs from Machine Translation Systems. In Proceedings of the Conference on Empirical Methods on Natural Language Processing, pages 98-107. Philipp Koehn. 2004. Statistical Significance Tests for Machine Translation Evaluation. In Proceedings of the Conference on Empirical Methods on Natural Language Processing, pages 388-395. Shankar Kumar and William Byrne. 2004. Minimum Bayes-Risk Decoding for Statistical Machine Translation. In Proceedings of the North American Association for Computational Linguistics, pages 169176. Shankar Kumar, Wolfgang Macherey, Chris Dyer, and Franz Och. 2009. Efficient Minimum Error Rate Training and Minimum Bayes-Risk Decoding for Translation Hypergraphs and Lattices. In Proceedings of the Association for Computational Linguistics, pages 163-171 . Mu Li, Nan Duan, Dongdong Zhang, Chi-Ho Li, and Ming Zhou. 2009a. Collaborative Decoding: Partial Hypothesis Re-Ranking Using Translation Consensus between Decoders. In Proceedings of the Association for Computational Linguistics, pages 585-592. Chi-Ho Li, Xiaodong He, Yupeng Liu, and Ning Xi. 2009b. Incremental HMM Alignment for MT system Combination. In Proceedings of the Association for Computational Linguistics, pages 949-957. Yang Liu, Haitao Mi, Yang Feng, and Qun Liu. 2009. Joint Decoding with Multiple Translation Models. In Proceedings of the Association for Computational Linguistics, pages 576-584. Franz Och. 2003. Minimum Error Rate Training in Statistical Machine Translation. In Proceedings of the Association for Computational Linguistics, pages 160-167. Franz Och and Hermann Ney. 2004. The Alignment Template Approach to Statistical Machine Translation. Computational Linguistics, 30(4): 417-449. Kishore Papineni, Salim Roukos, Todd Ward, and Weijing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In Proceedings of the Association for Computational Linguistics, pages 3 11-3 18. Libin Shen, Jinxi Xu, and Ralph Weischedel. 2008. A new String-to-Dependency Machine Translation Algorithm with a Target Dependency Language Model. In Proceedings of the Association for Computational Linguistics, pages 577-585. Antti-Veikko Rosti, Spyros Matsoukas, and Richard Schwartz. 2007. Improved Word-Level System Combination for Machine Translation. In Proceedings of the Association for Computational Linguistics, pages 3 12-3 19. 1267 Roy Tromble, Shankar Kumar, Franz Och, and Wolfgang Macherey. 2008. Lattice Minimum Bayes-Risk Decoding for Statistical Machine Translation. In Proceedings of the Conference on Empirical Methods on Natural Language Processing, pages 620629. Dekai Wu. 1997. Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora. Computational Linguistics, 23(3): 377-404. Deyi Xiong, Qun Liu, and Shouxun Lin. 2006. Maximum Entropy based Phrase Reordering Model for Statistical Machine Translation. In Proceedings of the Association for Computational Linguistics, pages 521-528. Yang Ye, Ming Zhou, and Chin-Yew Lin. 2007. Sentence Level Machine Translation Evaluation as a Ranking Problem: one step aside from BLEU. In Proceedings of the Second Workshop on Statistical Machine Translation, pages 240-247.