Reformulating Unsupervised Style Transfer as Paraphrase Generation

Kalpesh Krishna, John Wieting, Mohit Iyyer


Abstract
Modern NLP defines the task of style transfer as modifying the style of a given sentence without appreciably changing its semantics, which implies that the outputs of style transfer systems should be paraphrases of their inputs. However, many existing systems purportedly designed for style transfer inherently warp the input’s meaning through attribute transfer, which changes semantic properties such as sentiment. In this paper, we reformulate unsupervised style transfer as a paraphrase generation problem, and present a simple methodology based on fine-tuning pretrained language models on automatically generated paraphrase data. Despite its simplicity, our method significantly outperforms state-of-the-art style transfer systems on both human and automatic evaluations. We also survey 23 style transfer papers and discover that existing automatic metrics can be easily gamed and propose fixed variants. Finally, we pivot to a more real-world style transfer setting by collecting a large dataset of 15M sentences in 11 diverse styles, which we use for an in-depth analysis of our system.
Anthology ID:
2020.emnlp-main.55
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
737–762
Language:
URL:
https://aclanthology.org/2020.emnlp-main.55
DOI:
10.18653/v1/2020.emnlp-main.55
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.55.pdf
Optional supplementary material:
 2020.emnlp-main.55.OptionalSupplementaryMaterial.zip
Video:
 https://slideslive.com/38938942
Code
 martiansideofthemoon/style-transfer-paraphrase