  Long Short-Term Memory (LSTM) networks, a type of recurrent neural network
with a more complex computational unit, have been successfully applied to a
variety of sequence modeling tasks. In this paper we develop Tree Long
Short-Term Memory (TreeLSTM), a neural network model based on LSTM, which is
designed to predict a tree rather than a linear sequence. TreeLSTM defines the
probability of a sentence by estimating the generation probability of its
dependency tree. At each time step, a node is generated based on the
representation of the generated sub-tree. We further enhance the modeling power
of TreeLSTM by explicitly representing the correlations between left and right
dependents. Application of our model to the MSR sentence completion challenge
achieves results beyond the current state of the art. We also report results on
dependency parsing reranking achieving competitive performance.
