Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale

• URL: http://arxiv.org/abs/2403.08293v3
• Date: Mon, 17 Jun 2024 16:22:52 GMT
• Title: Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale
• Authors: Xiang Hu, Pengyu Ji, Qingyang Zhu, Wei Wu, Kewei Tu,
• Abstract summary: We present Generative Pretrained Structured Transformers (GPST), an unsupervised SLM at scale capable of being pre-trained from scratch on raw texts. GPST circumvents the limitations of previous SLMs such as relying on gold trees and sequential training. GPST significantly outperforms existing unsupervised SLMs on left-to-right grammar induction.
• Score: 36.584680344291556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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