Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer

Cicero Nogueira dos Santos, Igor Melnyk, Inkit Padhi


Abstract
We introduce a new approach to tackle the problem of offensive language in online social media. Our approach uses unsupervised text style transfer to translate offensive sentences into non-offensive ones. We propose a new method for training encoder-decoders using non-parallel data that combines a collaborative classifier, attention and the cycle consistency loss. Experimental results on data from Twitter and Reddit show that our method outperforms a state-of-the-art text style transfer system in two out of three quantitative metrics and produces reliable non-offensive transferred sentences.
Anthology ID:
P18-2031
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
189–194
Language:
URL:
https://aclanthology.org/P18-2031
DOI:
10.18653/v1/P18-2031
Bibkey:
Cite (ACL):
Cicero Nogueira dos Santos, Igor Melnyk, and Inkit Padhi. 2018. Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 189–194, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer (Nogueira dos Santos et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/P18-2031.pdf