title: Dependent Bidirectional RNN with Extended-long Short-term Memory
abstract: 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.
authors: Yuanhang Su Yuzhong Huang C.-C. Jay Kuo
hIndex: C.-C. Jay Kuo 78