Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training.

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ID: 30607
2018
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Abstract
This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. It is based on a Neural Network that considers input contexts of different length in order to ease the use of a fallback mechanism together with the precomputation of softmax normalization constants for these inputs. The proposed approach is empirically validated, showing their capability to emulate lower order N-grams with a single Neural Network. A machine translation task shows that the proposed model constitutes a good solution to the normalization cost of the output softmax layer of Neural Networks, for some practical cases, without a significant impact in performance while improving the system speed.
Reference Key
zamoramartnez2018fallbackplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zamora-Martínez, Francisco J;España-Boquera, Salvador;Castro-Bleda, Maria Jose;Palacios-Corella, Adrian;
Journal PloS one
Year 2018
DOI
DOI not found
URL
http://europepmc.org/articles/PMC6062053?pdf=render
Keywords Keywords not found

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