PersianPunc: A Large-Scale Dataset and BERT-Based Approach for Persian Punctuation Restoration

• URL: http://arxiv.org/abs/2603.05314v1
• Date: Thu, 05 Mar 2026 15:55:55 GMT
• Title: PersianPunc: A Large-Scale Dataset and BERT-Based Approach for Persian Punctuation Restoration
• Authors: Mohammad Javad Ranjbar Kalahroodi, Heshaam Faili, Azadeh Shakery,
• Abstract summary: Punctuation restoration is essential for improving the readability and downstream utility of automatic speech recognition outputs.<n>We introduce PersianPunc, a high-quality dataset of 17 million samples for Persian punctuation restoration.<n>We formulate punctuation restoration as a token-level sequence labeling task and fine-tune ParsBERT to achieve strong performance.
• Score: 3.763275651955603
• License: http://creativecommons.org/licenses/by/4.0/
• 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 (https://huggingface.co/datasets/MohammadJRanjbar/persian-punctuation-restoration) and model (https://huggingface.co/MohammadJRanjbar/parsbert-persian-punctuation) publicly available to facilitate future research in Persian NLP and provide a scalable framework applicable to other morphologically rich, low-resource languages.

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