  We propose a novel framework for controllable natural language
transformation. Realizing that the requirement of parallel corpus is
practically unsustainable for controllable generation tasks, an unsupervised
training scheme is introduced. The crux of the framework is a deep neural
encoder-decoder that is reinforced with text-transformation knowledge through
auxiliary modules (called scorers). The scorers, based on off-the-shelf
language processing tools, decide the learning scheme of the encoder-decoder
based on its actions. We apply this framework for the text-transformation task
of formalizing an input text by improving its readability grade; the degree of
required formalization can be controlled by the user at run-time. Experiments
on public datasets demonstrate the efficacy of our model towards: (a)
transforming a given text to a more formal style, and (b) introducing
appropriate amount of formalness in the output text pertaining to the input
control. Our code and datasets are released for academic use.
