In this work, we first conduct mathematical analysis on the memory, which is
 defined as a function that maps an element in a sequence to the current output,
 of three RNN cells; namely, the simple recurrent neural network (SRN), the long
 short-term memory (LSTM) and the gated recurrent unit (GRU). Based on the
 analysis, we propose a new design, called the extended-long short-term memory
 (ELSTM), to extend the memory length of a cell. Next, we present a multi-task
 RNN model that is robust to previous erroneous predictions, called the dependent
 bidirectional recurrent neural network (DBRNN), for the sequence-in-sequenceout
 (SISO) problem. Finally, the performance of the DBRNN model with the
 ELSTM cell is demonstrated by experimental results.
A recurrent neural network cell with extended-long short-term memory and a multi-task RNN model for sequence-in-sequence-out problems