Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A simple pattern-matching algorithm for recovering empty nodes and their antecedents
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
Accurate unlexicalized parsing
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
The Alignment Template Approach to Statistical Machine Translation
Computational Linguistics
Intricacies of Collins' Parsing Model
Computational Linguistics
Statistical Machine Translation with Scarce Resources Using Morpho-syntactic Information
Computational Linguistics
A phrase-based, joint probability model for statistical machine translation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Phrasal cohesion and statistical machine translation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Statistical machine translation by parsing
ACL '04 Proceedings of the 42nd 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
Clause restructuring for statistical machine translation
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
Improving a statistical MT system with automatically learned rewrite patterns
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Improving statistical MT through morphological analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Morphological analysis for statistical machine translation
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
An overview of probabilistic tree transducers for natural language processing
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
A tree-to-string phrase-based model for statistical machine translation
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Head finalization: a simple reordering rule for SOV languages
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
HPSG-Based Preprocessing for English-to-Japanese Translation
ACM Transactions on Asian Language Information Processing (TALIP)
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We present a phrase-based statistical machine translation approach which uses linguistic analysis in the preprocessing phase. The linguistic analysis includes morphological transformation and syntactic transformation. Since the word-order problem is solved using syntactic transformation, there is no reordering in the decoding phase. For morphological transformation, we use hand-crafted transformational rules. For syntactic transformation, we propose a transformational model based on a probabilistic context-free grammar. This model is trained using a bilingual corpus and a broad-coverage parser of the source language. This approach is applicable to language pairs in which the target language is poor in resources. We considered translation from English to Vietnamese and from English to French. Our experiments showed significant BLEU-score improvements in comparison with Pharaoh, a state-of-the-art phrase-based SMT system.