@inproceedings{bujnowski-etal-2020-empirical,
title = "An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation",
author = "Bujnowski, Pawel and
Ryzhova, Kseniia and
Choi, Hyungtak and
Witkowska, Katarzyna and
Piersa, Jaroslaw and
Krumholc, Tymoteusz and
Beksa, Katarzyna",
editor = "Clifton, Ann and
Napoles, Courtney",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
month = dec,
year = "2020",
address = "Online",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-industry.6",
doi = "10.18653/v1/2020.coling-industry.6",
pages = "50--63",
abstract = "The topic of this paper is neural multi-task training for text style transfer. We present an efficient method for neutral-to-style transformation using the transformer framework. We demonstrate how to prepare a robust model utilizing large paraphrases corpora together with a small parallel style transfer corpus. We study how much style transfer data is needed for a model on the example of two transformations: neutral-to-cute on internal corpus and modern-to-antique on publicly available Bible corpora. Additionally, we propose a synthetic measure for the automatic evaluation of style transfer models. We hope our research is a step towards replacing common but limited rule-based style transfer systems by more flexible machine learning models for both public and commercial usage.",
}
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%0 Conference Proceedings
%T An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation
%A Bujnowski, Pawel
%A Ryzhova, Kseniia
%A Choi, Hyungtak
%A Witkowska, Katarzyna
%A Piersa, Jaroslaw
%A Krumholc, Tymoteusz
%A Beksa, Katarzyna
%Y Clifton, Ann
%Y Napoles, Courtney
%S Proceedings of the 28th International Conference on Computational Linguistics: Industry Track
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Online
%F bujnowski-etal-2020-empirical
%X The topic of this paper is neural multi-task training for text style transfer. We present an efficient method for neutral-to-style transformation using the transformer framework. We demonstrate how to prepare a robust model utilizing large paraphrases corpora together with a small parallel style transfer corpus. We study how much style transfer data is needed for a model on the example of two transformations: neutral-to-cute on internal corpus and modern-to-antique on publicly available Bible corpora. Additionally, we propose a synthetic measure for the automatic evaluation of style transfer models. We hope our research is a step towards replacing common but limited rule-based style transfer systems by more flexible machine learning models for both public and commercial usage.
%R 10.18653/v1/2020.coling-industry.6
%U https://aclanthology.org/2020.coling-industry.6
%U https://doi.org/10.18653/v1/2020.coling-industry.6
%P 50-63
Markdown (Informal)
[An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation](https://aclanthology.org/2020.coling-industry.6) (Bujnowski et al., COLING 2020)
ACL