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arXiv · 2211.07712

Cloning Ideology and Style using Deep Learning

Omer Beg·Muhammad Nasir Zafar·Waleed Anjum
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Abstract

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.

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Omer Beg, Muhammad Nasir Zafar, Waleed Anjum. 2022-10-25. Cloning Ideology and Style using Deep Learning. https://arxiv.org/abs/2211.07712

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