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• W2169954957 abstract "In hierarchical phrase-based SMT systems, statistical models are integrated to guide the hierarchical rule selection for better translation performance. Previous work mainly focused on the selection of either the source side of a hierarchical rule or the target side of a hierarchical rule rather than considering both of them simultaneously. This paper presents a joint model to predict the selection of hierarchical rules. The proposed model is estimated based on four sub-models where the rich context knowledge from both source and target sides is leveraged. Our method can be easily incorporated into the practical SMT systems with the log-linear model framework. The experimental results show that our method can yield significant improvements in performance." @default.
• W2169954957 created "2016-06-24" @default.
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• W2169954957 date "2010-07-11" @default.
• W2169954957 modified "2023-10-04" @default.
• W2169954957 title "A Joint Rule Selection Model for Hierarchical Phrase-Based Translation" @default.
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