We propose a technique for learning representations of parser states in
transition-based dependency parsers. Our primary innovation is a new control
structure for sequence-to-sequence neural networks---the stack LSTM. Like the
conventional stack data structures used in transition-based parsing, elements
can be pushed to or popped from the top of the stack in constant time, but, in
addition, an LSTM maintains a continuous space embedding of the stack contents.
This lets us formulate an efficient parsing model that captures three facets of
a parser's state: (i) unbounded look-ahead into the buffer of incoming words,
(ii) the complete history of actions taken by the parser, and (iii) the
complete contents of the stack of partially built tree fragments, including
their internal structures. Standard backpropagation techniques are used for
training and yield state-of-the-art parsing performance.

该文提出了一种新的序列到序列神经网络控制结构 —— 堆栈 LSTM，用于学习过渡式依赖解析器的解析状态，将其应用于解析模型中，实现了解析器状态的三个方面，提高了解析性能。