  The standard LSTM, although it succeeds in the modeling long-range
dependences, suffers from a highly complex structure that can be simplified
through modifications to its gate units. This paper was to perform an empirical
comparison between the standard LSTM and three new simplified variants that
were obtained by eliminating input signal, bias and hidden unit signal from
individual gates, on the tasks of modeling two sequence datasets. The
experiments show that the three variants, with reduced parameters, can achieve
comparable performance with the standard LSTM. Due attention should be paid to
turning the learning rate to achieve high accuracies
