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

Recently, there has been an increasing interest in unsupervised parsers that
optimize semantically oriented objectives, typically using reinforcement
learning. Unfortunately, the learned trees often do not match actual syntax
trees well. Shen et al. (2018) propose a structured attention mechanism for
language modeling (PRPN), which induces better syntactic structures but relies
on ad hoc heuristics. Also, their model lacks interpretability as it is not
grounded in parsing actions. In our work, we propose an imitation learning
approach to unsupervised parsing, where we transfer the syntactic knowledge
induced by the PRPN to a Tree-LSTM model with discrete parsing actions. Its
policy is then refined by Gumbel-Softmax training towards a semantically
oriented objective. We evaluate our approach on the All Natural Language
Inference dataset and show that it achieves a new state of the art in terms of
parsing $F$-score, outperforming our base models, including the PRPN.

我们提出了一种模仿学习的无监督句法分析方法，将 PRPN 诱导的句法知识转移到带有离散句法分析操作的 Tree-LSTM 模型上，并通过 Gumbel-Softmax 训练不断优化其策略以实现更好的语义目标。在 All-NLI 数据集上进行实验，证明我们的方法在句法得分上超过了基准模型，包括 PRPN。

一种基于模仿学习的无监督解析方法

An Imitation Learning Approach to Unsupervised Parsing

Identifying implicit discourse relations between text spans is a challenging
task because it requires understanding the meaning of the text. To tackle this
task, recent studies have tried several deep learning methods but few of them
exploited the syntactic information. In this work, we explore the idea of
incorporating syntactic parse tree into neural networks. Specifically, we
employ the Tree-LSTM model and Tree-GRU model, which are based on the tree
structure, to encode the arguments in a relation. Moreover, we further leverage
the constituent tags to control the semantic composition process in these
tree-structured neural networks. Experimental results show that our method
achieves state-of-the-art performance on PDTB corpus.

本研究使用 Tree-LSTM 模型和 Tree-GRU 模型将句法分析树加入神经网络以编码关系中的参数，同时利用组成标记控制这些树状神经网络中的语义组合过程，实验结果表明我们的方法在 PDTB 语料库中达到了最先进的性能。