  Stylistic variation in text needs to be studied with different aspects
including the writer's personal traits, interpersonal relations, rhetoric, and
more. Despite recent attempts on computational modeling of the variation, the
lack of parallel corpora of style language makes it difficult to systematically
control the stylistic change as well as evaluate such models. We release
PASTEL, the parallel and annotated stylistic language dataset, that contains
~41K parallel sentences (8.3K parallel stories) annotated across different
personas. Each persona has different styles in conjunction: gender, age,
country, political view, education, ethnic, and time-of-writing. The dataset is
collected from human annotators with solid control of input denotation: not
only preserving original meaning between text, but promoting stylistic
diversity to annotators. We test the dataset on two interesting applications of
style language, where PASTEL helps design appropriate experiment and
evaluation. First, in predicting a target style (e.g., male or female in
gender) given a text, multiple styles of PASTEL make other external style
variables controlled (or fixed), which is a more accurate experimental design.
Second, a simple supervised model with our parallel text outperforms the
unsupervised models using nonparallel text in style transfer. Our dataset is
publicly available.
