  In this paper, we propose a test, called Flagged-1-Bit (F1B) test, to study
the intrinsic capability of recurrent neural networks in sequence learning.
Four different recurrent network models are studied both analytically and
experimentally using this test. Our results suggest that in general there
exists a conflict between feature selection and feature memorization in
sequence learning. Such a conflict can be resolved either using a gating
mechanism as in LSTM, or by increasing the state dimension as in Vanilla RNN.
Gated models resolve this conflict by adaptively adjusting their state-update
equations, whereas Vanilla RNN resolves this conflict by assigning different
dimensions different tasks. Insights into feature selection and memorization in
recurrent networks are given.
