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Dependent Bidirectional RNN with Extended-long Short-term Memory
Yuanhang Su, Yuzhong Huang, C.-C. Jay Kuo
Feb 15, 2018 (modified: Feb 15, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
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.
TL;DR:A recurrent neural network cell with extended-long short-term memory and a multi-task RNN model for sequence-in-sequence-out problems
Keywords:RNN, memory, LSTM, GRU, BRNN, encoder-decoder, Natural language processing
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