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• W1595052845 abstract "Neural network joint modeling (NNJM) has produced huge improvement in machine translation performance. As in standard neural network language modeling, a context-independent linear projection is applied to project a sparse input vector into a continuous representation at each word position. Because neighboring words are dependent on each other, context-independent projection may not be optimal. We propose a context-dependent linear projection approach which considers neighboring words. Experimental results showed that the proposed approach further improves NNJM by 0.5 BLEU for English-Iraqi Arabic translation in N-best rescoring. Compared to a baseline using hierarchical phrases and sparse features, NNJM with our proposed approach has achieved a 2 BLEU improvement." @default.
• W1595052845 created "2016-06-24" @default.
• W1595052845 creator A5036723791 @default.
• W1595052845 creator A5080359342 @default.
• W1595052845 date "2015-04-01" @default.
• W1595052845 modified "2023-09-22" @default.
• W1595052845 title "Neural network joint modeling via context-dependent projection" @default.
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• W1595052845 doi "https://doi.org/10.1109/icassp.2015.7178994" @default.
• W1595052845 hasPublicationYear "2015" @default.
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