Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Punctuation Restoration Improves Structure Understanding Without Supervision

Junghyun Min, Minho Lee, Woochul Lee, Yeonsoo Lee

TL;DR

This work tackles the gap between strong generative abilities of pre-trained language models and their understanding of linguistic structure. It introduces a punctuation restoration objective as an unsupervised pre-training signal that, when applied to a T5-based encoder, yields PR-T5 representations with improved structure awareness. Across 13 datasets and 7 tasks in generative, discriminative, and multitask settings, PR-T5 achieves consistent gains over a T5 baseline, with many improvements surpassing $0.02$ and several exceeding $0.05$, and it also enhances out-of-distribution generalization and stability across initializations. The approach requires no additional architecture and can be implemented with modest compute, offering a practical path to more robust structure-aware NLP representations in base-sized models.

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 $\blacktriangle$$\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.

Punctuation Restoration Improves Structure Understanding Without Supervision

TL;DR

This work tackles the gap between strong generative abilities of pre-trained language models and their understanding of linguistic structure. It introduces a punctuation restoration objective as an unsupervised pre-training signal that, when applied to a T5-based encoder, yields PR-T5 representations with improved structure awareness. Across 13 datasets and 7 tasks in generative, discriminative, and multitask settings, PR-T5 achieves consistent gains over a T5 baseline, with many improvements surpassing and several exceeding , and it also enhances out-of-distribution generalization and stability across initializations. The approach requires no additional architecture and can be implemented with modest compute, offering a practical path to more robust structure-aware NLP representations in base-sized models.

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 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.
Paper Structure (20 sections, 2 equations, 1 figure, 6 tables)

This paper contains 20 sections, 2 equations, 1 figure, 6 tables.

Table of Contents

  1. Introduction
  2. Objective and experimental setup
  3. Objective design
  4. Experimental setup
  5. Results
  6. Structure information encoding and use
  7. Performance stability and out-of-distribution generalization
  8. Discussion
  9. Additional details on experimental setup
  10. Discriminative approach
  11. Joint multitask generative approach
  12. Additional details on dataset
  13. Entity generation tasks
  14. Tag sequence generation tasks
  15. Sequence generation tasks
  16. ...and 5 more sections

Figures (1)

  • Figure 1: (a) The t5 architecture for a generative, text-to-text approach to NLP tasks. Here, we illustrate open information extraction. (b) A modification to the t5 architecture to allow a discriminative approach to NLP tasks. Here, we illustrate named entity recognition.