On the definition of toxicity in NLP

TL;DR

This paper proposes a new, objective, stress-level-based definition of toxicity in NLP to improve the robustness and accuracy of toxicity detection models, addressing the problem of vague and subjective existing definitions.

Contribution

It introduces a novel, stress-level-based toxicity definition and discusses its application in dataset creation and model training for NLP toxicity detection.

Findings

Proposes an objective, context-aware toxicity definition

Suggests methods for dataset creation based on stress levels

Aims to improve model robustness and accuracy

Abstract

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.