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
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Toward hierarchical models for statistical machine translation of inflected languages
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
Extensions to HMM-based statistical word alignment models
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Bridging the inflection morphology gap for Arabic statistical machine translation
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Context-dependent alignment models for statistical machine translation
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Getting to know Moses: initial experiments on German--English factored translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
English-to-Czech factored machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Improving statistical word alignments with morpho-syntactic transformations
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Statistical machine translation of german compound words
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
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In this paper, we present an approach to include morpho-syntactic dependencies into the training of the statistical alignment models. Existing statistical translation systems usually treat different derivations of the same base form as they were independent of each other. We propose a method which explicitly takes into account such in-terdependencies during the EM training of the statistical alignment models. The evaluation is done by comparing the obtained Viterbi alignments with a manually annotated reference alignment. The improvements of the alignment quality compared to the, to our knowledge, best system are reported on the German-English Verbmobil corpus.