  Author stylized rewriting is the task of rewriting an input text in a
particular author's style. Recent works in this area have leveraged
Transformer-based language models in a denoising autoencoder setup to generate
author stylized text without relying on a parallel corpus of data. However,
these approaches are limited by the lack of explicit control of target
attributes and being entirely data-driven. In this paper, we propose a
Director-Generator framework to rewrite content in the target author's style,
specifically focusing on certain target attributes. We show that our proposed
framework works well even with a limited-sized target author corpus. Our
experiments on corpora consisting of relatively small-sized text authored by
three distinct authors show significant improvements upon existing works to
rewrite input texts in target author's style. Our quantitative and qualitative
analyses further show that our model has better meaning retention and results
in more fluent generations.
