# Cloning Ideology and Style using Deep Learning

arXiv: https://arxiv.org/abs/2211.07712 · HTML: https://mlsanity.com/paper/2211.07712/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [MaskGAN: Better Text Generation via Filling in the______](https://mlsanity.com/paper/1801.07736/) (2018)
- [Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph](https://mlsanity.com/paper/2009.11692/) (2020)
- [Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints](https://mlsanity.com/paper/2005.00969/) (2020)
- [Exploring Transformers in Natural Language Generation: GPT, BERT, and XLNet](https://mlsanity.com/paper/2102.08036/) (2021)
- [Biomedical Data-to-Text Generation via Fine-Tuning Transformers](https://mlsanity.com/paper/2109.01518/) (2021)
- [Style Example-Guided Text Generation using Generative Adversarial Transformers](https://mlsanity.com/paper/2003.00674/) (2020)

## Abstract (arXiv)

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.