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We show an effective way of adding context information to shallow neural language models. We propose to use Subspace Multinomial Model (SMM) for context modeling and we add the extracted i-vectors in a computationally efficient way. By adding this information, we shrink the gap between shallow feed-forward network and an LSTM from 65 to 31 points of perplexity on the Wikitext-2 corpus (in the case of neural 5-gram model). Furthermore, we show that SMM i-vectors are suitable for domain adaptation and a very small amount of adaptation data (e.g. endmost 5% of a Wikipedia article) brings a substantial improvement. Our proposed changes are compatible
with most optimization techniques used for shallow feedforward LMs.

Added on December 19, 2018

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  • Contributed by : Individual
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Karel Benes,Santosh Kesiraju,Lukas Burget
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