Neural Networks Compression for Language Modeling

Artem M. Grachev; Dmitry I. Ignatov; Andrey V. Savchenko

doi:10.1007/978-3-319-69900-4_44

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arXiv · 1708.05963

Neural Networks Compression for Language Modeling

Artem M. Grachev·Dmitry I. Ignatov·Andrey V. Savchenko

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Abstract

In this paper, we consider several compression techniques for the language modeling problem based on recurrent neural networks (RNNs). It is known that conventional RNNs, e.g, LSTM-based networks in language modeling, are characterized with either high space complexity or substantial inference time. This problem is especially crucial for mobile applications, in which the constant interaction with the remote server is inappropriate. By using the Penn Treebank (PTB) dataset we compare pruning, quantization, low-rank factorization, tensor train decomposition for LSTM networks in terms of model size and suitability for fast inference.

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BibTeX RIS

Artem M. Grachev, Dmitry I. Ignatov, Andrey V. Savchenko. 2017-08-20. Neural Networks Compression for Language Modeling. https://doi.org/10.1007/978-3-319-69900-4_44

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