From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing
May 01, 2017 ยท Declared Dead ยท ๐ Inteligencia Artif.
"No code URL or promise found in abstract"
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Authors
Juan Andrรฉs Laura, Gabriel Masi, Luis Argerich
arXiv ID
1705.00697
Category
cs.CL: Computation & Language
Cross-listed
cs.AI,
cs.IT
Citations
6
Venue
Inteligencia Artif.
Last Checked
5 months ago
Abstract
In recent studies [1][13][12] Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, data compression is also based on predictions. What the problem comes down to is whether a data compressor could be used to perform as well as recurrent neural networks in natural language processing tasks. If this is possible,then the problem comes down to determining if a compression algorithm is even more intelligent than a neural network in specific tasks related to human language. In our journey we discovered what we think is the fundamental difference between a Data Compression Algorithm and a Recurrent Neural Network.
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