  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.
