  In this paper, we present Gamma-LSTM, an enhanced long short term memory
(LSTM) unit, to enable learning of hierarchical representations through
multiple stages of temporal abstractions. Gamma memory, a hierarchical memory
unit, forms the central memory of Gamma-LSTM with gates to regulate the
information flow into various levels of hierarchy, thus providing the unit with
a control to pick the appropriate level of hierarchy to process the input at a
given instant of time. We demonstrate better performance of Gamma-LSTM model
regular and stacked LSTMs in two settings (pixel-by-pixel MNIST digit
classification and natural language inference) placing emphasis on the ability
to generalize over long sequences.
