Title: AriEL: volume coding for sentence generation comparisons. Abstract: Saving sequences of data to a point in a continuous space makes it difficult to retrieve them via random sampling.  Mapping the input to a volume makes it easier, which is the strategy followed by Variational Autoencoders.  However optimizing for prediction and for smoothness, forces them to trade-off between the two.  We analyze the ability of standard deep learning techniques to generate sentences through latent space sampling.  We compare toAriEL, an entropic coding method to construct volumes without the need for extra loss terms. We benchmark on a toy grammar, to automatically evaluate the language learned and generated, and find where it is stored in the latent space. Then, we benchmark on a dataset of human dialogues and using GPT-2 inside AriEL. Our results indicate that the random access to stored information can be improved since AriEL is able to generate a wider variety of correct language by randomly sampling the latent space. This supports the hypothesis that encoding information into volumes, leads to improved retrieval of learned information with random sampling.