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- W2250445771 abstract "This paper tackles the sparsity problem in estimating phrase translation probabilities by learning continuous phrase representations, whose distributed nature enables the sharing of related phrases in their representations. A pair of source and target phrases are projected into continuous-valued vector representations in a low-dimensional latent space, where their translation score is computed by the distance between the pair in this new space. The projection is performed by a neural network whose weights are learned on parallel training data. Experimental evaluation has been performed on two WMT translation tasks. Our best result improves the performance of a state-of-the-art phrase-based statistical machine translation system trained on WMT 2012 French-English data by up to 1.3 BLEU points." @default.
- W2250445771 created "2016-06-24" @default.
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- W2250445771 date "2014-01-01" @default.
- W2250445771 modified "2023-09-24" @default.
- W2250445771 title "Learning Continuous Phrase Representations for Translation Modeling" @default.
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- W2250445771 doi "https://doi.org/10.3115/v1/p14-1066" @default.
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