Harnessing Attention Mechanisms: Efficient Sequence Reduction using
  Attention-based Autoencoders
Daniel Biermann Fabrizio Palumbo Morten Goodwin Ole-Christoffer Granmo
Many machine learning models use the manipulation of dimensions as a driving
force to enable models to identify and learn important features in data. In the
case of sequential data this manipulation usually happens on the token
dimension level. Despite the fact that many tasks require a change in sequence
length itself, the step of sequence length reduction usually happens out of
necessity and in a single step. As far as we are aware, no model uses the
sequence length reduction step as an additional opportunity to tune the models
performance. In fact, sequence length manipulation as a whole seems to be an
overlooked direction. In this study we introduce a novel attention-based method
that allows for the direct manipulation of sequence lengths. To explore the
method's capabilities, we employ it in an autoencoder model. The autoencoder
reduces the input sequence to a smaller sequence in latent space. It then aims
to reproduce the original sequence from this reduced form. In this setting, we
explore the methods reduction performance for different input and latent
sequence lengths. We are able to show that the autoencoder retains all the
significant information when reducing the original sequence to half its
original size. When reducing down to as low as a quarter of its original size,
the autoencoder is still able to reproduce the original sequence with an
accuracy of around 90%.