Abstract:
Text generation has played an important role in various applications of natural
language processing (NLP), and recent studies, researchers are paying
increasing attention to modeling and manipulating the style of the generation
text, which we call stylized text generation. In this tutorial, we will
provide a comprehensive literature review in this direction. We start from
the definition of style and different settings of stylized text generation,
illustrated with various applications. Then, we present different settings of
stylized generation, such as style-conditioned generation, style-transfer
generation, and style-adversarial generation. In each setting, we delve deep
into machine learning methods, including embedding learning techniques to
represent style, adversarial learning, and reinforcement learning with cycle
consistency to match content but to distinguish different styles. We also
introduce current approaches to evaluating stylized text generation systems.
We conclude our tutorial by presenting the challenges of stylized text
generation and discussing future directions, such as small-data training,
non-categorical style modeling, and a generalized scope of style transfer
(e.g., controlling the syntax as a style).