Probabilistic context-free grammars (PCFGs) with neural parameterization have
been shown to be effective in unsupervised phrase-structure grammar induction.
However, due to the cubic computational complexity of PCFG representation and
parsing, previous approaches cannot scale up to a relatively large number of
(nonterminal and preterminal) symbols. In this work, we present a new
parameterization form of PCFGs based on tensor decomposition, which has at most
quadratic computational complexity in the symbol number and therefore allows us
to use a much larger number of symbols. We further use neural parameterization
for the new form to improve unsupervised parsing performance. We evaluate our
model across ten languages and empirically demonstrate the effectiveness of
using more symbols. Our code: this https URL

本文介绍了一种基于张量分解的新的概率上下文无关文法参数化形式，其符号数量的计算复杂度最多为二次，适用于大量符号，可以进一步使用神经参数化来改善无监督解析性能。

PCFGs 能做得更好：用许多符号诱导概率上下文无关文法

PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with  Many Symbols

Most of the unsupervised dependency parsers are based on first-order
probabilistic generative models that only consider local parent-child
information. Inspired by second-order supervised dependency parsing, we
proposed a second-order extension of unsupervised neural dependency models that
incorporate grandparent-child or sibling information. We also propose a novel
design of the neural parameterization and optimization methods of the
dependency models. In second-order models, the number of grammar rules grows
cubically with the increase of vocabulary size, making it difficult to train
lexicalized models that may contain thousands of words. To circumvent this
problem while still benefiting from both second-order parsing and
lexicalization, we use the agreement-based learning framework to jointly train
a second-order unlexicalized model and a first-order lexicalized model.
Experiments on multiple datasets show the effectiveness of our second-order
models compared with recent state-of-the-art methods. Our joint model achieves
a 10% improvement over the previous state-of-the-art parser on the full WSJ
test set

我们提出了一种新的无监督神经依赖模型的二阶扩展，它可以包含祖父 - 子或兄弟信息，并使用基于一致性的学习框架联合训练一二阶无词汇化模型和一阶词汇化模型，实验表明我们的二阶模型在多个数据集上的效果优于最近的最先进方法，并且我们的联合模型在完整的 WSJ 测试集上取得了 10% 的改进。