  Text generation tasks have gotten the attention of researchers in the last
few years because of their applications on a large scale.In the past, many
researchers focused on task-based text generations.Our research focuses on text
generation based on the ideology and style of a specific author, and text
generation on a topic that was not written by the same author in the past.Our
trained model requires an input prompt containing initial few words of text to
produce a few paragraphs of text based on the ideology and style of the author
on which the model is trained.Our methodology to accomplish this task is based
on Bi-LSTM.The Bi-LSTM model is used to make predictions at the character
level, during the training corpus of a specific author is used along with the
ground truth corpus.A pre-trained model is used to identify the sentences of
ground truth having contradiction with the author's corpus to make our language
model inclined.During training, we have achieved a perplexity score of 2.23 at
the character level. The experiments show a perplexity score of around 3 over
the test dataset.
