This paper puts forward a new text to tensor representation that relies on information compression techniques to assign shorter codes to the most frequently used characters. This representation is language-independent with no need of pretraining and produces an encoding with no information loss. It provides an adequate description of the morphology of text, as it is able to represent prefixes, declensions, and inflections with similar vectors and are able to represent even unseen words on the training dataset. Similarly, as it is compact yet sparse, is ideal for speed up training times using tensor processing libraries. As part of this paper, we show that this technique is especially effective when coupled with convolutional neural networks (CNNs) for text classification at character-level. We apply two variants of CNN coupled with it. Experimental results show that it drastically reduces the number of parameters to be optimized, resulting in competitive classification accuracy values in only a fraction of the time spent by one-hot encoding representations, thus enabling training in commodity hardware.
The manuscript proposed to use prefix codes to compress the input to a neural network for text classification. It builds upon the work by Zhang & LeCun (2015) where the same tasks are used.
There are several issues with the paper and I cannot recommend acceptance of the paper in the current state. 
- It looks like it is not finished.
- the datasets are not described properly. 
- It is not clear to me where the baseline results come from.
 They do not match up to the Zhang paper (I have tried to find the matching accuracies there).
- It is not clear to me what the baselines actually are or how I can found more info on those.
- the results are not remarkable. 
Because of this, the paper needs to be updated and cleaned up before it can be properly reviewed. 
On top of this, I do not enjoy the style the paper is written in, the language is convoluted. 
For example: “The effort to use Neural Convolution Networks for text classification tasks is justified by the possibility of appropriating tools from the recent developments of techniques, libraries and hardware used especially in the image classification “
I do not know which message the paper tries to get across here. 
As a reviewer my impression (which is subjective) is that the authors used difficult language to make the manuscript look more impressive.
The acknowledgements should not be included here either.