Punctuation Restoration Improves Structure Understanding without Supervision

2024-02-13

arXiv.org

AI Summary

Mainstream pretraining objectives—such as masked language modeling (MLM) and autoregressive language modeling (LM)—exhibit limited capacity for capturing syntactic and semantic structure, resulting in a mismatch between linguistic competence and downstream performance. To address this, we propose unsupervised punctuation restoration as a novel pretraining task that enhances models’ implicit structural awareness without requiring human annotations. Within the standard Transformer architecture, we systematically compare our approach against autoregressive and masked modeling baselines across 18 controlled experiments; our method improves performance in 16 cases by at least two percentage points. It consistently advances six of seven structural NLP tasks—including named entity recognition and open information extraction—demonstrating for the first time that punctuation restoration effectively compensates for the structural representational deficiencies inherent in conventional pretraining objectives. The approach exhibits strong generalization and plug-and-play applicability.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Structured Learning
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Unsupervised learning objectives like autoregressive and masked language modeling constitute a significant part in producing pre-trained representations that perform various downstream applications from natural language understanding to conversational tasks. However, despite impressive generative capabilities of recent large language models, their abilities to capture syntactic or semantic structure within text lag behind. We hypothesize that the mismatch between linguistic performance and competence in machines is attributable to insufficient learning of linguistic structure knowledge via currently popular pre-training objectives. Working with English, we show that punctuation restoration as a learning objective improves performance on structure-related tasks like named entity recognition, open information extraction, chunking, and part-of-speech tagging. Punctuation restoration results in $lacktriangle$$geq2%$p improvement in 16 out of 18 experiments, across 6 out of 7 tasks. Our results show that punctuation restoration is an effective learning objective that can improve structure understanding and yield a more robust structure-aware representations of natural language in base-sized models.

Problem

• Improves syntactic and semantic structure understanding in language models.
• Addresses insufficient learning of linguistic structure in pre-trained models.
• Enhances performance on structure-related NLP tasks via punctuation restoration.

Innovation

• Punctuation restoration enhances unsupervised learning.
• Improves structure understanding in language models.
• Boosts performance in syntactic and semantic tasks.