Style versus Content: A distinction without a (learnable) difference?

Somayeh Jafaritazehjani, Gwénolé Lecorvé, Damien Lolive, John Kelleher


Abstract
Textual style transfer involves modifying the style of a text while preserving its content. This assumes that it is possible to separate style from content. This paper investigates whether this separation is possible. We use sentiment transfer as our case study for style transfer analysis. Our experimental methodology frames style transfer as a multi-objective problem, balancing style shift with content preservation and fluency. Due to the lack of parallel data for style transfer we employ a variety of adversarial encoder-decoder networks in our experiments. Also, we use of a probing methodology to analyse how these models encode style-related features in their latent spaces. The results of our experiments which are further confirmed by a human evaluation reveal the inherent trade-off between the multiple style transfer objectives which indicates that style cannot be usefully separated from content within these style-transfer systems.
Anthology ID:
2020.coling-main.197
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2169–2180
Language:
URL:
https://www.aclweb.org/anthology/2020.coling-main.197
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.coling-main.197.pdf