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Attentive Language Models

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Figure 1: Illustration of a step of the AttentiveRNN-LM with combined score. In this example,the model receives the third word as input (w3 ) af-ter storing the previous states (h1 and h2 ) in mem-ory
Table 1: Perplexity results over the PTB. Symbols: WT = weight tying (Press and Wolf, 2016); WD =weight decay and BD = Bayesian Dropout, both suggested by Gal and Ghahramani (2015).
Figure 2: Plot of attention weights for two sentences containing nominal modifiers. On the left columnare the attention weights calculated by the combined score
Figure 3: Plot of attention weights for two sentences containing relative clauses. On the left column arethe attention weights calculated by the combined score

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