Tree-structured neural networks have proven to be effective in learning
semantic representations by exploiting syntactic information. In spite of their
success, most existing models suffer from the underfitting problem: they
recursively use the same shared compositional function throughout the whole
compositional process and lack expressive power due to inability to capture the
richness of compositionality. In this paper, we address this issue by
introducing the dynamic compositional neural networks over tree structure
(DC-TreeNN), in which the compositional function is dynamically generated by a
meta network. The role of meta-network is to capture the metaknowledge across
the different compositional rules and formulate them. Experimental results on
two typical tasks show the effectiveness of the proposed models.

本文提出了基于动态组合神经网络的树状神经网络 (DC-TreeNN)，通过引入元网络动态生成组合函数，解决现有模型在组合过程中缺乏表达能力和容易欠拟合的问题，并在两个典型任务中取得了有效的实验结果。

树结构上的动态组合神经网络

Dynamic Compositional Neural Networks over Tree Structure

Tree-structured neural networks exploit valuable syntactic parse information
as they interpret the meanings of sentences. However, they suffer from two key
technical problems that make them slow and unwieldy for large-scale NLP tasks:
they usually operate on parsed sentences and they do not directly support
batched computation. We address these issues by introducing the Stack-augmented
Parser-Interpreter Neural Network (SPINN), which combines parsing and
interpretation within a single tree-sequence hybrid model by integrating
tree-structured sentence interpretation into the linear sequential structure of
a shift-reduce parser. Our model supports batched computation for a speedup of
up to 25 times over other tree-structured models, and its integrated parser can
operate on unparsed data with little loss in accuracy. We evaluate it on the
Stanford NLI entailment task and show that it significantly outperforms other
sentence-encoding models.

SPINN 集成树形结构的句子解释和移位规约分析器的线性序列结构中，支持大规模 NLP 任务的批量计算，并且在 Stanford NLI 蕴含任务中表现优异。

一种快速的句法分析和句子理解统一模型

A Fast Unified Model for Parsing and Sentence Understanding

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。