# Style Example-Guided Text Generation using Generative Adversarial Transformers

arXiv: https://arxiv.org/abs/2003.00674 · HTML: https://mlsanity.com/paper/2003.00674/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [MOVER: Mask, Over-generate and Rank for Hyperbole Generation](https://mlsanity.com/paper/2109.07726/) (2021)
- [Idiomatic Expression Paraphrasing without Strong Supervision](https://mlsanity.com/paper/2112.08592/) (2021)
- [From Solving a Problem Boldly to Cutting the Gordian Knot: Idiomatic Text Generation](https://mlsanity.com/paper/2104.06541/) (2021)
- [Generating Symbolic Reasoning Problems with Transformer GANs](https://mlsanity.com/paper/2110.10054/) (2021)
- [Memory-Augmented Generative Adversarial Transformers](https://mlsanity.com/paper/2402.19218/) (2024)
- [Cloning Ideology and Style using Deep Learning](https://mlsanity.com/paper/2211.07712/) (2022)

## Abstract (arXiv)

We introduce a language generative model framework for generating a styled paragraph based on a context sentence and a style reference example. The framework consists of a style encoder and a texts decoder. The style encoder extracts a style code from the reference example, and the text decoder generates texts based on the style code and the context. We propose a novel objective function to train our framework. We also investigate different network design choices. We conduct extensive experimental validation with comparison to strong baselines to validate the effectiveness of the proposed framework using a newly collected dataset with diverse text styles. Both code and dataset will be released upon publication.