Processing sentence constituency trees in binarised form is a common and
popular approach in literature. However, constituency trees are non-binary by
nature. The binarisation procedure changes deeply the structure, furthering
constituents that instead are close. In this work, we introduce a new approach
to deal with non-binary constituency trees which leverages tensor-based models.
In particular, we show how a powerful composition function based on the
canonical tensor decomposition can exploit such a rich structure. A key point
of our approach is the weight sharing constraint imposed on the factor
matrices, which allows limiting the number of model parameters. Finally, we
introduce a Tree-LSTM model which takes advantage of this composition function
and we experimentally assess its performance on different NLP tasks.

文章介绍了一种新的处理非二叉短语结构树的方法，其利用张量模型和基于张量分解的组合函数，并通过约束限制因子矩阵的参数数量来优化模型性能。同时，文章提出了一种基于该方法的 Tree-LSTM 模型，并在不同自然语言处理任务上进行了实验评估。

通过张量分解学习非二元句法分析树

Learning from Non-Binary Constituency Trees via Tensor Decomposition

Most existing recursive neural network (RvNN) architectures utilize only the
structure of parse trees, ignoring syntactic tags which are provided as
by-products of parsing. We present a novel RvNN architecture that can provide
dynamic compositionality by considering comprehensive syntactic information
derived from both the structure and linguistic tags. Specifically, we introduce
a structure-aware tag representation constructed by a separate tag-level
tree-LSTM. With this, we can control the composition function of the existing
word-level tree-LSTM by augmenting the representation as a supplementary input
to the gate functions of the tree-LSTM. In extensive experiments, we show that
models built upon the proposed architecture obtain superior or competitive
performance on several sentence-level tasks such as sentiment analysis and
natural language inference when compared against previous tree-structured
models and other sophisticated neural models.

提出一种考虑语法标签的递归神经网络 (RvNN) 架构，通过引入结构感知的标签表示，可以控制现有单词级树 LSTM 的组合函数，进而在情感分析和自然语言推理等句子级任务中取得了较优或有竞争力的表现。