A systematic comparison of various statistical alignment models
Computational Linguistics
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
A class-based approach to word alignment
Computational Linguistics
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
A probability model to improve word alignment
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
Inducing translation lexicons via diverse similarity measures and bridge languages
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Alignment model adaptation for domain-specific word alignment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Stochastic lexicalized inversion transduction grammar for alignment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Word alignment for languages with scarce resources
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Improved HMM alignment models for languages with scarce resources
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Aligning words in English-Hindi parallel corpora
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Pivot language approach for phrase-based statistical machine translation
Machine Translation
A Chinese-Japanese Lexical Machine Translation through a Pivot Language
ACM Transactions on Asian Language Information Processing (TALIP)
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This paper proposes an approach to improve word alignment for languages with scarce resources using bilingual corpora of other language pairs. To perform word alignment between languages L1 and L2, we introduce a third language L3. Although only small amounts of bilingual data are available for the desired language pair L1-L2, large-scale bilingual corpora in L1-L3 and L2-L3 are available. Based on these two additional corpora and with L3 as the pivot language, we build a word alignment model for L1 and L2. This approach can build a word alignment model for two languages even if no bilingual corpus is available in this language pair. In addition, we build another word alignment model for L1 and L2 using the small L1-L2 bilingual corpus. Then we interpolate the above two models to further improve word alignment between L1 and L2. Experimental results indicate a relative error rate reduction of 21.30% as compared with the method only using the small bilingual corpus in L1 and L2.