  Tree-structured LSTM is promising way to consider long-distance interaction
over hierarchies. However, there have been few research efforts on the
hyperparameter tuning of the construction and traversal of tree-structured
LSTM. To name a few, hyperparamters such as the interval of state
initialization, the number of batches for normalization have been left
unexplored specifically in applying batch normalization for reducing training
cost and parallelization. In this paper, we propose a novel recursive algorithm
for traversing batch normalized tree-structured LSTM. In proposal method, we
impose the constraint on the recursion algorithm for the depth-first search of
binary tree representation of LSTM for which batch normalization is applied.
With our constrained recursion, we can control the hyperparameter in the
traversal of several tree-structured LSTMs which is generated in the process of
batch normalization. The tree traversal is divided into two steps. At first
stage, the width-first search over models is applied for discover the start
point of the latest tree-structured LSTM block. Then, the depth-first search is
run to traverse tree-structured LSTM. Proposed method enables us to explore the
optimized selection of hyperparameters of recursive neural network
implementation by changing the constraints of our recursion algorithm. In
experiment, we measure and plot the validation loss and computing time with
changing the length of internal of state initialization of tree-structured
LSTM. It has been turned out that proposal method is effective for
hyperparameter tuning such as the number of batches and length of interval of
state initialization of tree-structured LSTM.
