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Research map / Architectures and efficiency / Attention and transformers

Pointer Sentinel Mixture Models

ICLR 2017 · Architectures and efficiency › Attention and transformers · approach: Language model pretraining and induction · 460 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add a pointer sentinel mixture to Zoneout + Variational LSTM (ablation)Zoneout + Variational LSTM · Penn Treebank testperplexity: 80.6 -> 70.9Helped
add a pointer sentinel mixture to Zoneout + Variational LSTM (ablation)Zoneout + Variational LSTM · Penn Treebank validationperplexity: 84.4 -> 72.4Helped
add a pointer sentinel mixture to Zoneout + Variational LSTM (ablation)Zoneout + Variational LSTM · WikiText-2 testperplexity: 100.9 -> 80.8Helped
add a pointer sentinel mixture to Zoneout + Variational LSTM (ablation)Zoneout + Variational LSTM · WikiText-2 validationperplexity: 108.7 -> 84.8Helped
use the pointer sentinel mixture instead of a large LSTMPenn Treebank · Penn Treebank testperplexity: 78.4 -> 70.9Helped
use the pointer sentinel mixture instead of a Variational RHNPenn Treebank · Penn Treebank testperplexity: 71.3 -> 70.9Helped

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