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Text style transfer aims to controllably generate text with targetedstylistic changes while maintaining core meaning from the source sentenceconstant. Many of the existing style transfer benchmarks primarily focus onindividual high-level semantic changes (e.g. positive to negative), whichenable controllability at a high level but do not offer fine-grained controlinvolving sentence structure, emphasis, and content of the sentence. In thispaper, we introduce a large-scale benchmark, StylePTB, with (1) pairedsentences 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-grainedstylistic changes as building blocks for more complex, high-level transfers. Bybenchmarking existing methods on StylePTB, we find that they struggle to modelfine-grained changes and have an even more difficult time composing multiplestyles. As a result, StylePTB brings novel challenges that we hope willencourage future research in controllable text style transfer, compositionalmodels, and learning disentangled representations. Solving these challengeswould present important steps towards controllable text generation.

StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer