@inproceedings{tran-etal-2020-towards,
title = "Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redaction",
author = "Tran, Minh and
Zhang, Yipeng and
Soleymani, Mohammad",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.190",
doi = "10.18653/v1/2020.coling-main.190",
pages = "2107--2114",
abstract = "Offensive and abusive language is a pressing problem on social media platforms. In this work, we propose a method for transforming offensive comments, statements containing profanity or offensive language, into non-offensive ones. We design a Retrieve, Generate and Edit unsupervised style transfer pipeline to redact the offensive comments in a word-restricted manner while maintaining a high level of fluency and preserving the content of the original text. We extensively evaluate our method{'}s performance and compare it to previous style transfer models using both automatic metrics and human evaluations. Experimental results show that our method outperforms other models on human evaluations and is the only approach that consistently performs well on all automatic evaluation metrics.",
}
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<abstract>Offensive and abusive language is a pressing problem on social media platforms. In this work, we propose a method for transforming offensive comments, statements containing profanity or offensive language, into non-offensive ones. We design a Retrieve, Generate and Edit unsupervised style transfer pipeline to redact the offensive comments in a word-restricted manner while maintaining a high level of fluency and preserving the content of the original text. We extensively evaluate our method’s performance and compare it to previous style transfer models using both automatic metrics and human evaluations. Experimental results show that our method outperforms other models on human evaluations and is the only approach that consistently performs well on all automatic evaluation metrics.</abstract>
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%0 Conference Proceedings
%T Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redaction
%A Tran, Minh
%A Zhang, Yipeng
%A Soleymani, Mohammad
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F tran-etal-2020-towards
%X Offensive and abusive language is a pressing problem on social media platforms. In this work, we propose a method for transforming offensive comments, statements containing profanity or offensive language, into non-offensive ones. We design a Retrieve, Generate and Edit unsupervised style transfer pipeline to redact the offensive comments in a word-restricted manner while maintaining a high level of fluency and preserving the content of the original text. We extensively evaluate our method’s performance and compare it to previous style transfer models using both automatic metrics and human evaluations. Experimental results show that our method outperforms other models on human evaluations and is the only approach that consistently performs well on all automatic evaluation metrics.
%R 10.18653/v1/2020.coling-main.190
%U https://aclanthology.org/2020.coling-main.190
%U https://doi.org/10.18653/v1/2020.coling-main.190
%P 2107-2114
Markdown (Informal)
[Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redaction](https://aclanthology.org/2020.coling-main.190) (Tran et al., COLING 2020)
ACL