@inproceedings{emmery-etal-2018-style,
title = "Style Obfuscation by Invariance",
author = "Emmery, Chris and
Manjavacas Arevalo, Enrique and
Chrupa{\l}a, Grzegorz",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1084",
pages = "984--996",
abstract = "The task of obfuscating writing style using sequence models has previously been investigated under the framework of obfuscation-by-transfer, where the input text is explicitly rewritten in another style. A side effect of this framework are the frequent major alterations to the semantic content of the input. In this work, we propose obfuscation-by-invariance, and investigate to what extent models trained to be explicitly style-invariant preserve semantics. We evaluate our architectures in parallel and non-parallel settings, and compare automatic and human evaluations on the obfuscated sentences. Our experiments show that the performance of a style classifier can be reduced to chance level, while the output is evaluated to be of equal quality to models applying style-transfer. Additionally, human evaluation indicates a trade-off between the level of obfuscation and the observed quality of the output in terms of meaning preservation and grammaticality.",
}
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%0 Conference Proceedings
%T Style Obfuscation by Invariance
%A Emmery, Chris
%A Manjavacas Arevalo, Enrique
%A Chrupała, Grzegorz
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 aug
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F emmery-etal-2018-style
%X The task of obfuscating writing style using sequence models has previously been investigated under the framework of obfuscation-by-transfer, where the input text is explicitly rewritten in another style. A side effect of this framework are the frequent major alterations to the semantic content of the input. In this work, we propose obfuscation-by-invariance, and investigate to what extent models trained to be explicitly style-invariant preserve semantics. We evaluate our architectures in parallel and non-parallel settings, and compare automatic and human evaluations on the obfuscated sentences. Our experiments show that the performance of a style classifier can be reduced to chance level, while the output is evaluated to be of equal quality to models applying style-transfer. Additionally, human evaluation indicates a trade-off between the level of obfuscation and the observed quality of the output in terms of meaning preservation and grammaticality.
%U https://aclanthology.org/C18-1084
%P 984-996
Markdown (Informal)
[Style Obfuscation by Invariance](https://aclanthology.org/C18-1084) (Emmery et al., COLING 2018)
ACL
- Chris Emmery, Enrique Manjavacas Arevalo, and Grzegorz Chrupała. 2018. Style Obfuscation by Invariance. In Proceedings of the 27th International Conference on Computational Linguistics, pages 984–996, Santa Fe, New Mexico, USA. Association for Computational Linguistics.