Style Example-Guided Text Generation using Generative Adversarial Transformers
This addresses the challenge of controlled text generation for applications requiring specific stylistic outputs, though it appears incremental in combining existing techniques.
The authors tackled the problem of generating styled paragraphs from a context sentence and style reference example by introducing a framework with a style encoder and text decoder, achieving state-of-the-art results on a newly collected dataset with diverse text styles.
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.