StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer
Yiwei Lyu*, Paul Pu Liang*, Hai Pham*, Eduard Hovy, Barnabas Poczos, Ruslan Salakhutdinov, and Louis-Philippe Morency
NAACL 2021. (*equal contribution)
If you find this repository useful, please cite our paper:
@article{lyu2021styleptb,
title={StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer},
author={Lyu, Yiwei and Liang, Paul Pu and Pham, Hai and Hovy, Eduard and P{\'o}czos, Barnab{\'a}s and Salakhutdinov, Ruslan and Morency, Louis-Philippe},
journal={arXiv preprint arXiv:2104.05196},
year={2021}
}
First check that the requirements are satisfied:
Python 3.6
torch 1.2.0
numpy 1.18.1
The next step is to clone the repository:
git clone https://github.com/lvyiwei1/StylePTB.gitTo checkout single style transfers, use single_transform_checkout.py with the three letter style code as follows:
python single_transform_checkout.py [3-letter style code]
After you run the script, the data will be contained in a folder with the 3-letter code as name.
3 letter style codes:
TFU == To Future
TPA == To Past
TPR == To Present
ATP == Active To Passive
PTA == Passive To Active
PFB == PP Front To Back
PBF == PP Back To Front
IAD == Information Addition
ARR == ADJ/ADV Remooval
SBR == Substatement Removal
PPR == PP Removal
AEM == ADJ Emphasis
VEM == Verb Emphasis
NSR == Noun Synonym Replacement
ASR == Adjective Synonym Replacement
VSR == Verb Synonym Replacement
NAR == Noun Antonym Replacement
AAR == Adjective Antonym Replacement
VAR == Verb Antonym Replacement
LFS == Least Frequent Synonym Replacement
MFS == Most Frequent Synonym Replacement
To access the compositional datasets, a few of them are provided in the "Compositional Datasets" folder. No checkout needed.
The scripts for GPT baseline, GRU+attn, Retrieve-Edit, and StyleGPT are in the "Model Codes" folder. Note that the code for Retrieve-Edit is taken directly from the codes provided by the authors of "A Retrieve-and-Edit Framework for Predicting Structured Outputs" (NeurIPS2018)
We used maluuba nlg-eval to evaluate model performance using automated metrics. See https://github.com/Maluuba/nlg-eval for instructions on evaluation
The scripts used to perform automated transfers with parse trees are in the "Automatic Transfer Scripts", and the webpages and full results of the human annotated transfers are in "Amazon Mechanical Turk Webpages and Results" folder.