  The vanilla LSTM has become one of the most potential architectures in
word-level language modeling, like other recurrent neural networks, overfitting
is always a key barrier for its effectiveness. The existing noise-injected
regularizations introduce the random noises of fixation intensity, which
inhibits the learning of the RNN throughout the training process. In this
paper, we propose a new structure-expanding regularization method called
Adjective Noise Injection (ANI), which considers the output of an extra RNN
branch as a kind of adaptive noises and injects it into the main-branch RNN
output. Due to the adaptive noises can be improved as the training processes,
its negative effects can be weakened and even transformed into a positive
effect to further improve the expressiveness of the main-branch RNN. As a
result, ANI can regularize the RNN in the early stage of training and further
promoting its training performance in the later stage. We conduct experiments
on three widely-used corpora: PTB, WT2, and WT103, whose results verify both
the regularization and promoting the training performance functions of ANI.
Furthermore, we design a series simulation experiments to explore the reasons
that may lead to the regularization effect of ANI, and we find that in training
process, the robustness against the parameter update errors can be strengthened
when the LSTM equipped with ANI.
