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Pyramidal Recurrent Unit for Language Modeling

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Figure 1: Comparison of training (solid lines) andvalidation (dashed lines) perplexities on the PennTreebank with standard dropout for pyramidal re-current units (PRU) and LSTM
Figure 2: Block diagram visualizing the transformations in pyramidal recurrent unit (left) and the LSTM(bottom right) along with the LSTM gating architecture (top right)
Table 1: Comparison of single model word-level perplexity of our model with state-of-the-art on vali-dation and test sets of Penn Treebank and Wikitext-2 dataset
Figure 3: Histogram of the entropies of next-tokendistributions predicted by the PRU (mean 3.80)and the LSTM (mean 3.93) on the PTB validationset
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