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We propose a simplified model of attention which is applicable tofeed-forward neural networks and demonstrate that the resulting model can solvethe synthetic "addition" and "multiplication" long-term memory problems forsequence lengths which are both longer and more widely varying than the bestpublished results for these tasks.

Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems