From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer

John Pavlopoulos, Leo Laugier, Alexandros Xenos, Jeffrey Sorensen, Ion Androutsopoulos


Abstract
We study the task of toxic spans detection, which concerns the detection of the spans that make a text toxic, when detecting such spans is possible. We introduce a dataset for this task, ToxicSpans, which we release publicly. By experimenting with several methods, we show that sequence labeling models perform best, but methods that add generic rationale extraction mechanisms on top of classifiers trained to predict if a post is toxic or not are also surprisingly promising. Finally, we use ToxicSpans and systems trained on it, to provide further analysis of state-of-the-art toxic to non-toxic transfer systems, as well as of human performance on that latter task. Our work highlights challenges in finer toxicity detection and mitigation.
Anthology ID:
2022.acl-long.259
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3721–3734
Language:
URL:
https://aclanthology.org/2022.acl-long.259
DOI:
10.18653/v1/2022.acl-long.259
Bibkey:
Cite (ACL):
John Pavlopoulos, Leo Laugier, Alexandros Xenos, Jeffrey Sorensen, and Ion Androutsopoulos. 2022. From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3721–3734, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer (Pavlopoulos et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.259.pdf
Code
 ipavlopoulos/toxic_spans