  In sequence modeling tasks the token order matters, but this information can
be partially lost due to the discretization of the sequence into data points.
In this paper, we study the imbalance between the way certain token pairs are
included in data points and others are not. We denote this a token order
imbalance (TOI) and we link the partial sequence information loss to a
diminished performance of the system as a whole, both in text and speech
processing tasks. We then provide a mechanism to leverage the full token order
information -Alleviated TOI- by iteratively overlapping the token composition
of data points. For recurrent networks, we use prime numbers for the batch size
to avoid redundancies when building batches from overlapped data points. The
proposed method achieved state of the art performance in both text and speech
related tasks.
