DeTexD: A Benchmark Dataset for Delicate Text Detection

Serhii Yavnyi, Oleksii Sliusarenko, Jade Razzaghi, Olena Nahorna, Yichen Mo, Knar Hovakimyan, Artem Chernodub


Abstract
Over the past few years, much research has been conducted to identify and regulate toxic language. However, few studies have addressed a broader range of sensitive texts that are not necessarily overtly toxic. In this paper, we introduce and define a new category of sensitive text called “delicate text.” We provide the taxonomy of delicate text and present a detailed annotation scheme. We annotate DeTexD, the first benchmark dataset for delicate text detection. The significance of the difference in the definitions is highlighted by the relative performance deltas between models trained each definitions and corpora and evaluated on the other. We make publicly available the DeTexD Benchmark dataset, annotation guidelines, and baseline model for delicate text detection.
Anthology ID:
2023.woah-1.2
Volume:
The 7th Workshop on Online Abuse and Harms (WOAH)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Yi-ling Chung, Paul R{\"ottger}, Debora Nozza, Zeerak Talat, Aida Mostafazadeh Davani
Venue:
WOAH
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14–28
Language:
URL:
https://aclanthology.org/2023.woah-1.2
DOI:
10.18653/v1/2023.woah-1.2
Bibkey:
Cite (ACL):
Serhii Yavnyi, Oleksii Sliusarenko, Jade Razzaghi, Olena Nahorna, Yichen Mo, Knar Hovakimyan, and Artem Chernodub. 2023. DeTexD: A Benchmark Dataset for Delicate Text Detection. In The 7th Workshop on Online Abuse and Harms (WOAH), pages 14–28, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
DeTexD: A Benchmark Dataset for Delicate Text Detection (Yavnyi et al., WOAH 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.woah-1.2.pdf