On the definition of toxicity in NLP
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Abstract (arXiv)
The fundamental problem in toxicity detection task lies in the fact that the toxicity is ill-defined. This causes us to rely on subjective and vague data in models' training, which results in non-robust and non-accurate results: garbage in - garbage out. This work suggests a new, stress-level-based definition of toxicity designed to be objective and context-aware. On par with it, we also describe possible ways of applying this new definition to dataset creation and model training.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 25 references that are in the map. First posted on arXiv 3 Oct 2023.
Builds on
6 of 25 references.
References · 6
- 2021Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detectioncited by 264
- 2020Toxicity Detection: Does Context Really Matter?cited by 93
- 2022Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotationcited by 69
- 2022Handling Bias in Toxic Speech Detection: A Surveycited by 61
- 2021Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detectioncited by 19
- 2021The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classificationcited by 18
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Citing papers per year: 2024: 1.
1 paper.