indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languages
Reviewed by Pithpith:K2FJF4NUopen to challenge →
read the original abstract
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
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources
A unified survey that consolidates Indian NLP resources by task, language, domain, and modality while identifying gaps in coverage and generalization.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.