Translating collocations for bilingual lexicons: a statistical approach
Computational Linguistics
A stochastic finite-state word-segmentation algorithm for Chinese
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A systematic comparison of various statistical alignment models
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Models of translational equivalence among words
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The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Combining clues for word alignment
EACL '03 Proceedings of the tenth conference on European chapter of the Association for 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
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
HHMM-based Chinese lexical analyzer ICTCLAS
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
HMM word and phrase alignment for statistical machine translation
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
MTTK: an alignment toolkit for statistical machine translation
NAACL-Demonstrations '06 Proceedings of the 2006 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume: demonstrations
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Bayesian semi-supervised Chinese word segmentation for statistical machine translation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Lattice-based minimum error rate training for statistical machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Improving word alignment using syntactic dependencies
SSST '08 Proceedings of the Second Workshop on Syntax and Structure in Statistical Translation
Improved statistical machine translation by multiple Chinese word segmentation
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Optimizing Chinese word segmentation for machine translation performance
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Pseudo-word for phrase-based machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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We introduce a bilingually motivated word segmentation approach to languages where word boundaries are not orthographically marked, with application to Phrase-Based Statistical Machine Translation (PB-SMT). Our approach is motivated from the insight that PB-SMT systems can be improved by optimizing the input representation to reduce the predictive power of translation models. We firstly present an approach to optimize the existing segmentation of both source and target languages for PB-SMT and demonstrate the effectiveness of this approach using a Chinese--English MT task, that is, to measure the influence of the segmentation on the performance of PB-SMT systems. We report a 5.44% relative increase in Bleu score and a consistent increase according to other metrics. We then generalize this method for Chinese word segmentation without relying on any segmenters and show that using our segmentation PB-SMT can achieve more consistent state-of-the-art performance across two domains. There are two main advantages of our approach. First of all, it is adapted to the specific translation task at hand by taking the corresponding source (target) language into account. Second, this approach does not rely on manually segmented training data so that it can be automatically adapted for different domains.