  Text style transfer aims to controllably generate text with targeted
stylistic changes while maintaining core meaning from the source sentence
constant. Many of the existing style transfer benchmarks primarily focus on
individual high-level semantic changes (e.g. positive to negative), which
enable controllability at a high level but do not offer fine-grained control
involving sentence structure, emphasis, and content of the sentence. In this
paper, we introduce a large-scale benchmark, StylePTB, with (1) paired
sentences undergoing 21 fine-grained stylistic changes spanning atomic lexical,
syntactic, semantic, and thematic transfers of text, as well as (2)
compositions of multiple transfers which allow modeling of fine-grained
stylistic changes as building blocks for more complex, high-level transfers. By
benchmarking existing methods on StylePTB, we find that they struggle to model
fine-grained changes and have an even more difficult time composing multiple
styles. As a result, StylePTB brings novel challenges that we hope will
encourage future research in controllable text style transfer, compositional
models, and learning disentangled representations. Solving these challenges
would present important steps towards controllable text generation.
