Text generation tasks have gotten the attention of researchers in the last
few years because of their applications on a large this http URL 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.

该研究使用 Bi-LSTM 模型基于特定作者的思想和风格生成主题非该作者编写的一篇文章。在训练语料库中包含特定作者的作品和现成的真实语料库以及使用预训练模型识别矛盾的语句，实现了字符级别的 perplexity 得分。