The need for tree structure modelling on top of sequence modelling is an open
issue in neural dependency parsing. We investigate the impact of adding a tree
layer on top of a sequential model by recursively composing subtree
representations (composition) in a transition-based parser that uses features
extracted by a BiLSTM. Composition seems superfluous with such a model,
suggesting that BiLSTMs capture information about subtrees. We perform model
ablations to tease out the conditions under which composition helps. When
ablating the backward LSTM, performance drops and composition does not recover
much of the gap. When ablating the forward LSTM, performance drops less
dramatically and composition recovers a substantial part of the gap, indicating
that a forward LSTM and composition capture similar information. We take the
backward LSTM to be related to lookahead features and the forward LSTM to the
rich history-based features both crucial for transition-based parsers. To
capture history-based information, composition is better than a forward LSTM on
its own, but it is even better to have a forward LSTM as part of a BiLSTM. We
correlate results with language properties, showing that the improved lookahead
of a backward LSTM is especially important for head-final languages.

研究神经依赖解析中树形结构模型需要在序列模型之上添加的必要性，通过双向 LSTM 提取的特性进行了模型基础，证明前向 LSTM 和组合提取相似信息并且组合提取更适用于基于转移的解析器。