RabindraNet, Creating Literary Works in the Style of Rabindranath Tagore
This work addresses the problem of limited computational literary analysis for Bengali literature enthusiasts and researchers, though it is incremental as it applies existing methods to a new domain.
The authors tackled the lack of NLP analysis for Bengali literature by introducing RabindraNet, a character-level RNN model with stacked-LSTM layers trained on Rabindranath Tagore's works to generate literary texts in his style across multiple genres, and they compiled and published an open-source dataset of his digitized works on Kaggle.
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.