  Recurrent Neural Networks (RNN) received a vast amount of attention last
decade. Recently, the architectures of Recurrent AutoEncoders (RAE) found many
applications in practice. RAE can extract the semantically valuable
information, called context that represents a latent space useful for further
processing. Nevertheless, recurrent autoencoders are hard to train, and the
training process takes much time. In this paper, we propose an autoencoder
architecture with sequence-aware encoding, which employs 1D convolutional layer
to improve its performance in terms of model training time. We prove that the
recurrent autoencoder with sequence-aware encoding outperforms a standard RAE
in terms of training speed in most cases. The preliminary results show that the
proposed solution dominates over the standard RAE, and the training process is
order of magnitude faster.
