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
Contextual dependencies in unsupervised word segmentation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Introducing a Translation Dictionary into Phrase-Based SMT
IEICE - Transactions on Information and Systems
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
Incremental adaptation of speech-to-speech translation
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Web-Based Transliteration of Person Names
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Bayesian unsupervised word segmentation with nested Pitman-Yor language modeling
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
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Noun dropping and mis-translations occasionally occurs with Machine Translation (MT) output. These errors can cause communication problems between system users. Some of the MT architectures are able to incorporate bilingual noun lexica, which can improve the translation quality of sentences which include nouns. In this paper, we proposed an automatic method to enable a monolingual user to add new words to the lexicon. In the experiments, we compare the proposed method to three other methods. According to the experimental results, the proposed method gives the best performance in both point of view of Character Error Rate (CER) and Word Error Rate (WER). The improvement from using only a transliteration system is very large, about 13 points in CER and 32 points in WER.