  Long Short-Term Memory (LSTM) units have the ability to memorise and use
long-term dependencies between inputs to generate predictions on time series
data. We introduce the concept of modifying the cell state (memory) of LSTMs
using rotation matrices parametrised by a new set of trainable weights. This
addition shows significant increases of performance on some of the tasks from
the bAbI dataset.
