From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing

Juan Andres Laura, Gabriel Omar Masi, Luis Argerich
<span title="2018-03-21">2018</span> <i title="IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5qve3fgne5dh3mzbzovoilyufm" style="color: black;">Inteligencia Artificial</a> </i> &nbsp;
In recent studies 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 prediction. What the problem comes down to is whether a data compressor could be used to perform as well as recurrent neural networks in the natural language processing tasks of sentiment analysis and automatic text generation. If this is possible, then the problem comes down
more &raquo; ... determining if a compression algorithm is even more intelligent than a neural network in such tasks. In our journey, a fundamental difference between a Data Compression Algorithm and Recurrent Neural Networks has been discovered.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4114/intartif.vol21iss61pp30-46">doi:10.4114/intartif.vol21iss61pp30-46</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rwqclrrxnfconh6yvq3jhl4k64">fatcat:rwqclrrxnfconh6yvq3jhl4k64</a> </span>
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