Natural Language Processing
PersianPunc: A Large-Scale Dataset and BERT-Based Approach for Persian Punctuation Restoration
This research introduces PersianPunc, a significant new dataset and a fine-tuned BERT-based approach for Persian punctuation restoration. It addresses critical gaps in available resources and demonstrates superior performance compared to general-purpose large language models, particularly in maintaining text integrity and computational efficiency.
Executive Impact & Key Metrics
Our model achieves industry-leading accuracy for Persian punctuation, underpinned by a massive, meticulously curated dataset. This translates to more reliable and efficient NLP pipelines for enterprises operating in Persian.
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Macro-averaged F1 Score
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High-Quality Samples
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Full Sentence Match Rate (Our Model)
Deep Analysis & Enterprise Applications
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Data Curation and Analysis
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. Our methodology includes detailed preprocessing, quality filtering, and train/validation/test splits, ensuring representativeness across diverse domains.
Specialized BERT Model
Our fine-tuned ParsBERT model achieves a macro-averaged F1-score of 91.33% and a micro-averaged F1-score of 97.28% on the test set. It performs exceptionally well on periods (98.71%) and strongly on colons (90.45%) and question marks (88.89%), demonstrating robust performance across various punctuation types.
Advantages over LLMs
Compared to large language models like GPT-4o, our ParsBERT model achieves superior macro F1 (91.33% vs 85.96%) and a significantly higher Full Sentence Match Rate (61.80% vs 50.10%). LLMs often exhibit over-correction tendencies (removing/replacing words, fixing grammar), which is problematic for ASR post-processing where source text integrity is crucial. Additionally, our lightweight BERT-based approach requires substantially lower computational resources, making it suitable for real-time applications.
Enterprise Process Flow: PersianPunc Dataset Curation Workflow
Systematic Aggregation
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Preprocessing & Filtering
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Deduplication
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Train/Validation/Test Splits
91.33%
Macro-averaged F1-score for Persian Punctuation Restoration
| Feature | Our ParsBERT Model | GPT-4o (Large Language Model) |
|---|---|---|
| Macro F1-score | 91.33% | 85.96% |
| Full Sentence Match (FSM) Rate | 61.80% | 50.10% |
| Over-correction tendency | Low (only adds punctuation) | High (removes/replaces words, fixes spelling/grammar) |
| Computational Resources | Low | High |
| Suitability for Real-time ASR Post-processing | High | Low (due to over-correction) |
Critical Impact on Persian Semantics
Problem: Without punctuation: "bakhshesh lazem nist e'damesh konid"
(Meaning: "No mercy needed, execute him")
Solution: With punctuation: "bakhshesh, lazem nist e'damesh konid"
(Meaning: "Forgiveness, no need to execute him")
Benefit: As illustrated in Figure 1, even minimal punctuation changes in Persian can dramatically alter semantic interpretation, transforming sentence meaning from negative to positive sentiment. This underscores the critical importance of accurate punctuation restoration for preserving original meaning and enabling reliable downstream NLP tasks like sentiment analysis and machine translation.
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