mlsanityData to 7 Oct 2026

indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languages

arXiv 2203.16825 ↗First posted 31 Mar 2022cs.CLcited by 3

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Abstract (arXiv)

Automatic Speech Recognition (ASR) generates text which is most of the times devoid of any punctuation. Absence of punctuation is text can affect readability. Also, down stream NLP tasks such as sentiment analysis, machine translation, greatly benefit by having punctuation and sentence boundary information. We present an approach for automatic punctuation of text using a pretrained IndicBERT model. Inverse text normalization is done by hand writing weighted finite state transducer (WFST) grammars. We have developed this tool for 11 Indic languages namely Hindi, Tamil, Telugu, Kannada, Gujarati, Marathi, Odia, Bengali, Assamese, Malayalam and Punjabi. All code and data is publicly. available

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 5 of its 14 references that are in the map. First posted on arXiv 31 Mar 2022.

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5 of 14 references.

References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  4. 2021NeMo Inverse Text Normalization: From Development To Productioncited by 28
  5. 2020Adversarial Transfer Learning for Punctuation Restorationcited by 15

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Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languagescited by 52
  2. 2024Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languagescited by 4
  3. 2025Mark My Words: A Robust Multilingual Model for Punctuation in Text and Speech Transcriptscited by 2