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.

本文提出了一种名为 Tree Long Short-Term Memory (TreeLSTM) 的神经网络模型，它基于 LSTM 并专门用于预测树结构，同时通过明确表示左右句法相关性，提高了建模能力。在 MSR 句子完成挑战和依存分析重排序方面的应用，都达到了当前最先进技术水平的效果。