Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale

TL;DR

GPST is an unsupervised, scalable syntactic language model that jointly learns to generate sentences and parse trees, outperforming previous models in language understanding, generation, and grammar induction.

Contribution

Introduces GPST, a novel unsupervised structured transformer model that enables parallel training and surpasses prior models in multiple NLP tasks.

Findings

Outperforms GPT-2 in various language tasks

Significantly better grammar induction results

Faster training compared to existing SLMs

Abstract

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.