Abstract
Punctuation restoration is essential for improving the readability and downstream utility of automatic speech recognition (ASR) outputs, yet remains underexplored for Persian despite its importance. We introduce PersianPunc, a large-scale, high-quality dataset of 17 million samples for Persian punctuation restoration, constructed through systematic aggregation and filtering of existing textual resources. We formulate punctuation restoration as a token-level sequence labeling task and fine-tune ParsBERT to achieve strong performance. Through comparative evaluation, we demonstrate that while large language models can perform punctuation restoration, they suffer from critical limitations: over-correction tendencies that introduce undesired edits beyond punctuation insertion (particularly problematic for speech-to-text pipelines) and substantially higher computational requirements. Our lightweight BERT-based approach achieves a macro-averaged F1 score of 91.33% on our test set while maintaining efficiency suitable for real-time applications. We make our dataset (this https URL) and model (this https URL) publicly available to facilitate future research in Persian NLP and provide a scalable framework applicable to other morphologically rich, low-resource languages.
Abstract (translated)
标点符号恢复对于提高自动语音识别（ASR）输出的可读性和下游应用的实用性至关重要，然而，在波斯语中这一领域尚未得到充分探索。我们推出了PersianPunc，这是一个大规模、高质量的数据集，包含1700万个样本，旨在解决波斯语文本标点符号恢复问题，通过系统性地聚合和筛选现有文本资源构建而成。我们将标点符号恢复定义为一个标记级序列标注任务，并针对此任务微调了ParsBERT模型以实现强大性能。通过比较评估我们发现，虽然大型语言模型能够执行标点符号恢复，但它们存在关键局限：过度校正倾向导致引入除插入标点之外的不必要修改（特别是在语音转文本管道中尤为严重），并且需要更高的计算资源。我们的基于轻量级BERT的方法在测试集上实现了91.33%的宏平均F1分数，并且保持了适用于实时应用的效率。 我们公开发布了数据集（[此链接](https://this https URL)）和模型（[此链接](https://this https URL)），以促进未来波斯语自然语言处理领域的研究，同时也为其他形态丰富、资源有限的语言提供了一种可扩展框架。
URL
https://arxiv.org/abs/2603.05314
