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A systematic comparison of various statistical alignment models
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NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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Learning non-isomorphic tree mappings for machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Tree-to-string alignment template for statistical machine translation
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Hierarchical Phrase-Based Translation
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Forest-based translation rule extraction
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Forest-based tree sequence to string translation model
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 1 - Volume 1
Improving tree-to-tree translation with packed forests
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Non-projective parsing for statistical machine translation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Synchronous tree adjoining machine translation
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Fast translation rule matching for syntax-based statistical machine translation
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Augmenting string-to-tree translation models with fuzzy use of source-side syntax
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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This paper studies two issues, non-isomorphic structure translation and target syntactic structure usage, for statistical machine translation in the context of forest-based tree to tree sequence translation. For the first issue, we propose a novel non-isomorphic translation framework to capture more non-isomorphic structure mappings than traditional tree-based and tree-sequence-based translation methods. For the second issue, we propose a parallel space searching method to generate hypothesis using tree-to-string model and evaluate its syntactic goodness using tree-to-tree/tree sequence model. This not only reduces the search complexity by merging spurious-ambiguity translation paths and solves the data sparseness issue in training, but also serves as a syntax-based target language model for better grammatical generation. Experiment results on the benchmark data show our proposed two solutions are very effective, achieving significant performance improvement over baselines when applying to different translation models.