Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach

Michael Wiegand, Elisabeth Eder, Josef Ruppenhofer


Abstract
We address the task of distinguishing implicitly abusive sentences on identity groups (“Muslims contaminate our planet”) from other group-related negative polar sentences (“Muslims despise terrorism”). Implicitly abusive language are utterances not conveyed by abusive words (e.g. “bimbo” or “scum”). So far, the detection of such utterances could not be properly addressed since existing datasets displaying a high degree of implicit abuse are fairly biased. Following the recently-proposed strategy to solve implicit abuse by separately addressing its different subtypes, we present a new focused and less biased dataset that consists of the subtype of atomic negative sentences about identity groups. For that task, we model components that each address one facet of such implicit abuse, i.e. depiction as perpetrators, aspectual classification and non-conformist views. The approach generalizes across different identity groups and languages.
Anthology ID:
2022.naacl-main.410
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5600–5612
Language:
URL:
https://aclanthology.org/2022.naacl-main.410
DOI:
10.18653/v1/2022.naacl-main.410
Bibkey:
Cite (ACL):
Michael Wiegand, Elisabeth Eder, and Josef Ruppenhofer. 2022. Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5600–5612, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach (Wiegand et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.410.pdf
Code
 miwieg/naacl2022_identity_groups
Data
FrameNet