Punctuation Restoration Improves Structure Understanding without Supervision (2025)

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 ▲≥2%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.

## introduction the modern natural language processing paradigm centers around transformer-based pre-trained language models (plms; @xcite ; radford et al. (2018); devlin et al. (2019) ). they are optimized on masked language modeling (mlm) and autoregressive language modeling, which provide powerful representations to approach various problems in natural language processing. it is no exaggeration that language models have become effective in tasks like named entity recognition (ner), information extraction, semantic role labeling (srl) that require understanding of syntactic, semantic, and discourse structure @xcite . however, the following suggests there is still room for improvement in current language models' abilities to understand such structure in natural language to perform downstream tasks reliably and robustly. ## results we measure the effects of punctuation restoration as an additional pre-training objective on downstream tasks on t5-base, with the four behaviors outlined in section 1 in mind. in this section, we find direct evidence that this method helps mitigate three out of four behaviors we describe in section 1. we report our results in tables 1, 2, 3. each reported value of precision, recall, and f1 represents an average over the same 5 seed initializations, with the exception of discriminative ner, where we analyze 15 seed initializations. ## discussion results from section 3 support our hypothesis that complementing mlm with a more structure-related objective improves structure understanding. in par-ticular, we use a punctuation restoration objective, described in section 2 and evaluate with various structure-related tasks. while it is difficult to investigate the exact mechanism of how additional training on punctuation restoration improves learned representations, we attempt to provide an explanation. in section 1, we analyze that current methods for representation learning during the pre-training stage lack sufficient signal, and hypothesize additional training with a structure-sensitive objective should improve structure understanding. much like how prosody helps disambiguate syntax in human speech processing @xcite , punctuation can be a useful guide in syntax disambiguation, and eventually toward forming a robust representation of text. punctuation marks represent prosodic design @xcite which carries linguistic information @xcite . punctuation marks often also indicate syntactic or semantic boundaries @xcite . optimizing a computational system to predict punctuation allows it to predict syntactic and semantic boundaries, even in the absence of punctuation in the original text. sufficient training in restoring punctuation can imitate effects of explicitly providing a parse, facilitating natural language understanding via a stronger understanding of sentence structure. performance improvement from punctuation 123 restoration is not limited to a specific dataset, task, and setting 3 . and represents an overall increase in representation robustness, as we observe out-ofdistribution performance jump in ner, openie, and chunking. because of the wide range of experiments in which improvement is observed, we interpret this to be a general improvement of structure understanding rather than fortunate task-specific artifacts from the additional training. our methods yield a more reliable and robust representation that can be easily implemented and do not interfere with architectural additions. punctuation restoration can be applied to reinforce structure understanding and improve robustness of learned representations regardless of model choice, or taskspecific engineering policy. the effective objective requires no supervision, and one can construct a training corpus with little computational or manual resources.