What is it about?
This study proposes an efficient approach to predict punctuations in Bangla text, using a Bidirectional Recurrent Neural Network (BRNN) and an attention model trained on large datasets. The study applies extensive postprocessing techniques to improve the model's accuracy in predicting punctuations, achieving promising results for both imbalanced and balanced datasets. This research work contributes significantly to the NLP field for Bangla language, paving the way for further research in the future.
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Why is it important?
Punctuation prediction is crucial for enhancing the readability of machine-transcribed speeches or texts by adding appropriate punctuations. However, systems like Automatic Speech Recognizer (ASR) produce unpunctuated texts, making it difficult for humans to read and also hinder the performance of various Natural Language Processing (NLP) tasks. While such tasks have been thoroughly investigated for English, very limited work has been done for punctuation prediction in the Bangla language. The proposed approach in this research work, which utilizes a BRNN and attention model with postprocessing techniques for predicting punctuation in Bangla text, is significant as it enhances the readability of Bangla text and produces promising results for both imbalanced and balanced datasets, which demonstrates the efficacy of the proposed approach and contribute significantly to the NLP field for Bangla language. Additionally, this research work lays the foundation for future research in this direction.
Perspectives
Punctuation prediction for Bangla language is one of the topics that needed attention, and we have done so by achieving significant results obtained by our proposed model. This article shall pave the way for further research in the arena of Bangla NLP as a whole. The Bangla plain text datasets will eventually facilitate more related research works to a wider range of spectrum.
Rezwan Rahin
Punctuation prediction model is crucial as it can help improve the quality of machine translation, speech recognition, and natural language processing systems. In our work, we have focused on the performance of BRNN model to predict punctuation in Bangla text. Our results show promising potential and we believe, this work represents a significant step forward in improving language processing for underrepresented languages like Bangla.
Habibur Rahman
United International University
Read the Original
This page is a summary of: Punctuation Prediction in Bangla Text, ACM Transactions on Asian and Low-Resource Language Information Processing, March 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3575804.
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