Automatic grammar generation from two different perspectives
Automatic grammar generation from two different perspectives
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning non-isomorphic tree mappings for machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Dependency treelet translation: syntactically informed phrasal SMT
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Machine translation using probabilistic synchronous dependency insertion grammars
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Maximum entropy based phrase reordering model for statistical machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Tree-to-string alignment template for statistical machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A path-based transfer model for machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Translating with non-contiguous phrases
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
SPMT: statistical machine translation with syntactified target language phrases
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
The impact of parse quality on syntactically-informed statistical machine translation
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Parsing the penn chinese treebank with semantic knowledge
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Constituency to dependency translation with forests
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A novel dependency-to-string model for statistical machine translation
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Soft dependency constraints for reordering in hierarchical phrase-based translation
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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This paper describes a novel model using dependency structures on the source side for syntax-based statistical machine translation: Dependency Treelet String Correspondence Model (DTSC). The DTSC model maps source dependency structures to target strings. In this model translation pairs of source treelets and target strings with their word alignments are learned automatically from the parsed and aligned corpus. The DTSC model allows source treelets and target strings with variables so that the model can generalize to handle dependency structures with the same head word but with different modifiers and arguments. Additionally, target strings can be also discontinuous by using gaps which are corresponding to the uncovered nodes which are not included in the source treelets. A chart-style decoding algorithm with two basic operations--substituting and attaching--is designed for the DTSC model. We argue that the DTSC model proposed here is capable of lexicalization, generalization, and handling discontinuous phrases which are very desirable for machine translation. We finally evaluate our current implementation of a simplified version of DTSC for statistical machine translation.