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• W2294953265 abstract "We propose the use of neural networks to model source-side preordering for faster and better statistical machine translation. The neural network trains a logistic regression model to predict whether two sibling nodes of the source-side parse tree should be swapped in order to obtain a more monotonic parallel corpus, based on samples extracted from the word-aligned parallel corpus. For multiple language pairs and domains, we show that this yields the best reordering performance against other state-of-the-art techniques, resulting in improved translation quality and very fast decoding." @default.
• W2294953265 created "2016-06-24" @default.
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• W2294953265 date "2015-01-01" @default.
• W2294953265 modified "2023-09-28" @default.
• W2294953265 title "Fast and Accurate Preordering for SMT using Neural Networks" @default.
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• W2294953265 doi "https://doi.org/10.3115/v1/n15-1105" @default.
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