  Recursive neural networks (RNN) and their recently proposed extension
recursive long short term memory networks (RLSTM) are models that compute
representations for sentences, by recursively combining word embeddings
according to an externally provided parse tree. Both models thus, unlike
recurrent networks, explicitly make use of the hierarchical structure of a
sentence. In this paper, we demonstrate that RNNs nevertheless suffer from the
vanishing gradient and long distance dependency problem, and that RLSTMs
greatly improve over RNN's on these problems. We present an artificial learning
task that allows us to quantify the severity of these problems for both models.
We further show that a ratio of gradients (at the root node and a focal leaf
node) is highly indicative of the success of backpropagation at optimizing the
relevant weights low in the tree. This paper thus provides an explanation for
existing, superior results of RLSTMs on tasks such as sentiment analysis, and
suggests that the benefits of including hierarchical structure and of including
LSTM-style gating are complementary.
