Tree-structured neural networks encode a particular tree geometry for a
sentence in the network design. However, these models have at best only
slightly outperformed simpler sequence-based models. We hypothesize that neural
sequence models like LSTMs are in fact able to discover and implicitly use
recursive compositional structure, at least for tasks with clear cues to that
structure in the data. We demonstrate this possibility using an artificial data
task for which recursive compositional structure is crucial, and find an
LSTM-based sequence model can indeed learn to exploit the underlying tree
structure. However, its performance consistently lags behind that of tree
models, even on large training sets, suggesting that tree-structured models are
more effective at exploiting recursive structure.

本论文通过对人工数据任务的实验，研究了基于树结构和基于序列的神经网络模型对于递归组合结构的应用效果。结果表明，基于序列的 LSTM 模型同样能够识别该结构并进行相应运用，但树结构模型在处理这种结构时表现更为出色。