A syntactic language model (SLM) incrementally generates a sentence with its syntactic tree in a left-to-right manner. We present Generative Pretrained Structured Transformers (GPST), an unsupervised SLM at scale capable of being pre-trained from scratch on raw texts with high parallelism. GPST circumvents the limitations of previous SLMs such as relying on gold trees and sequential training. It consists of two components, a usual SLM supervised by a uni-directional language modeling loss, and an additional composition model, which induces syntactic parse trees and computes constituent representations, supervised by a bi-directional language modeling loss. We propose a representation surrogate to enable joint parallel training of the two models in a hard-EM fashion. We pre-train GPST on OpenWebText, a corpus with $9$ billion tokens, and demonstrate the superiority of GPST over GPT-2 with a comparable size in numerous tasks covering both language understanding and language generation. Meanwhile, GPST also significantly outperforms existing unsupervised SLMs on left-to-right grammar induction, while holding a substantial acceleration on training.

翻译：句法语言模型（SLM）以从左到右的方式逐步生成句子及其句法树。我们提出生成式预训练结构化Transformer（GPST），这是一种可在大规模原始文本上以高度并行方式从零开始预训练的无监督SLM。GPST克服了以往SLM依赖金标准句法树和顺序训练的局限性。它由两个组件构成：一个由单向语言建模损失监督的标准SLM，以及一个附加的合成模型——该模型诱导句法解析树并计算成分表示，由双向语言建模损失监督。我们提出一种表示代理机制，以硬EM方式实现两个模型的联合并行训练。我们在包含90亿个token的OpenWebText语料库上预训练GPST，并在涵盖语言理解与语言生成的多个任务中，证明GPST在模型规模相当的情况下优于GPT-2。同时，GPST在从左到右的语法归纳任务上显著超越现有无监督SLM，且训练速度大幅提升。