RabindraNet, Creating Literary Works in the Style of Rabindranath Tagore

TL;DR

RabindraNet is a character-level RNN trained on Tagore's works to generate literary texts mimicking his style, aiming to advance NLP-based literary analysis and generation in Bengali literature.

Contribution

The paper introduces RabindraNet, a novel character-level RNN model trained on a new open-source dataset of Tagore's works for Bengali text generation.

Findings

Successfully generated texts in Tagore's style across genres

Created a comprehensive dataset of Tagore's works for NLP tasks

Demonstrated potential for Bengali literary analysis and AI-based text creation

Abstract

Bengali literature has a rich history of hundreds of years with luminary figures such as Rabindranath Tagore and Kazi Nazrul Islam. However, analytical works involving the most recent advancements in NLP have barely scratched the surface utilizing the enormous volume of the collected works from the writers of the language. In order to bring attention to the analytical study involving the works of Bengali writers and spearhead the text generation endeavours in the style of existing literature, we are introducing RabindraNet, a character level RNN model with stacked-LSTM layers trained on the works of Rabindranath Tagore to produce literary works in his style for multiple genres. We created an extensive dataset as well by compiling the digitized works of Rabindranath Tagore from authentic online sources and published as open source dataset on data science platform Kaggle.