Computational Linguistics
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Transliteration of proper names in cross-lingual information retrieval
MultiNER '03 Proceedings of the ACL 2003 workshop on Multilingual and mixed-language named entity recognition - Volume 15
A joint source-channel model for machine transliteration
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A generic framework for machine transliteration
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Transliteration as constrained optimization
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Report of NEWS 2009 machine transliteration shared task
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Improving transliteration with precise alignment of phoneme chunks and using contextual features
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
Report of NEWS 2010 transliteration generation shared task
NEWS '10 Proceedings of the 2010 Named Entities Workshop
Reranking with multiple features for better transliteration
NEWS '10 Proceedings of the 2010 Named Entities Workshop
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Transliteration of given parallel name entities can be formulated as a phrase-based statistical machine translation (SMT) process, via its routine procedure comprising training, optimization and decoding. In this paper, we present our approach to transliterating name entities using the loglinear phrase-based SMT on character sequences. Our proposed work improves the translation by using bidirectional models, plus some heuristic guidance integrated in the decoding process. Our evaluated results indicate that this approach performs well in all standard runs in the NEWS2009 Machine Transliteration Shared Task.