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MohammadJRanjbar/PersianPunc

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The declared licence (cc-by-4.0) is classified as permissive.

Declared licence: cc-by-4.0, from the tagEvidence: README.md at c7dfd68
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Citation text from the card

@inproceedings{kalahroodi-etal-2026-persianpunc,
    title = "{P}ersian{P}unc: A Large-Scale Dataset and {BERT}-Based Approach for {P}ersian Punctuation Restoration",
    author = "Kalahroodi, Mohammad Javad Ranjbar  and
      Faili, Heshaam  and
      Shakery, Azadeh",
    editor = "Merchant, Rayyan  and
      Megerdoomian, Karine",
    booktitle = "The Proceedings of the First Workshop on {NLP} and {LLM}s for the {I}ranian Language Family",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.silkroadnlp-1.11/",
    doi = "10.18653/v1/2026.silkroadnlp-1.11",
    pages = "105--113",
    ISBN = "979-8-89176-371-5",
    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 undesir

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dataset_licence({ "id": "MohammadJRanjbar/PersianPunc" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0