@inproceedings{chernodub-etal-2023-detexd,
    title = "{D}e{T}ex{D}: A Benchmark Dataset for Delicate Text Detection",
    author = "Yavnyi, Serhii  and
      Sliusarenko, Oleksii  and
      Razzaghi, Jade  and
      Nahorna, Olena  and
      Mo, Yichen  and
      Hovakimyan, Knar  and
      Chernodub, Artem",
    editor = {Chung, Yi-ling  and
      R{{\textbackslash}"ottger}, Paul  and
      Nozza, Debora  and
      Talat, Zeerak  and
      Mostafazadeh Davani, Aida},
    booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.woah-1.2",
    doi = "10.18653/v1/2023.woah-1.2",
    pages = "14--28",
    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.",
}

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.
Yavnyi, Serhii Sliusarenko, Oleksii Razzaghi, Jade Nahorna, Olena Mo, Yichen Hovakimyan, Knar Chernodub, Artem
DeTexD: A Benchmark Dataset for Delicate Text Detection