The nature of statistical learning theory
The nature of statistical learning theory
A maximum entropy approach to natural language processing
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A systematic comparison of various statistical alignment models
Computational Linguistics
Computational Linguistics
Automatic English-Chinese name transliteration for development of multilingual resources
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Translating named entities using monolingual and bilingual resources
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
A joint source-channel model for machine transliteration
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Named entity transliteration with comparable corpora
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Weakly supervised named entity transliteration and discovery from multilingual comparable corpora
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Direct orthographical mapping for machine transliteration
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Discriminative methods for transliteration
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Transliteration as constrained optimization
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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
Revisiting pivot language approach for machine translation
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
Report of NEWS 2009 machine transliteration shared task
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Whitepaper of NEWS 2009 machine transliteration shared task
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Everybody loves a rich cousin: an empirical study of transliteration through bridge languages
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
How do you pronounce your name?: improving G2P with transliterations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Leveraging supplemental representations for sequential transduction
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Syllable-based machine transliteration with extra phrase features
NEWS '12 Proceedings of the 4th Named Entity Workshop
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This paper presents two pivot strategies for statistical machine transliteration, namely system-based pivot strategy and model-based pivot strategy. Given two independent source-pivot and pivot-target name pair corpora, the model-based strategy learns a direct source-target transliteration model while the system-based strategy learns a source-pivot model and a pivot-target model, respectively. Experimental results on benchmark data show that the system-based pivot strategy is effective in reducing the high resource requirement of training corpus for low-density language pairs while the model-based pivot strategy performs worse than the system-based one.