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Triplet lexicon models for statistical machine translation
2008
Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08
unpublished
This paper describes a lexical trigger model for statistical machine translation. We present various methods using triplets incorporating long-distance dependencies that can go beyond the local context of phrases or n-gram based language models. We evaluate the presented methods on two translation tasks in a reranking framework and compare it to the related IBM model 1. We show slightly improved translation quality in terms of BLEU and TER and address various constraints to speed up the
doi:10.3115/1613715.1613764
fatcat:qm7ok5u6gvckdg5ojxvkqlxseq