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。