Scaling dense PCFGs to thousands of nonterminals via a low-rank
parameterization of the rule probability tensor has been shown to be beneficial
for unsupervised parsing. However, PCFGs scaled this way still perform poorly
as a language model, and even underperform similarly-sized HMMs. This work
introduces \emph{SimplePCFG}, a simple PCFG formalism with independent left and
right productions. Despite imposing a stronger independence assumption than the
low-rank approach, we find that this formalism scales more effectively both as
a language model and as an unsupervised parser. As an unsupervised parser, our
simple PCFG obtains an average F1 of 65.1 on the English PTB, and as a language
model, it obtains a perplexity of 119.0, outperforming similarly-sized low-rank
PCFGs. We further introduce \emph{FlashInside}, a hardware IO-aware
implementation of the inside algorithm for efficiently scaling simple PCFGs.

通过低秩参数化规则概率张量，将稠密 PCFG 扩展到数千个非终结符，有助于无监督解析。本文提出了一种简单的 PCFG 形式，独立的左右生成，这种形式比低秩方法更有效地扩展为语言模型和无监督解析器，并且具有更好的性能。同时，我们还提出了一种硬件 IO 感知的内部算法实现，用于高效扩展简单的 PCFG。