NLP tasks like hate speech detection are subjective: annotators disagree about what the correct data labels are. We propose two contrasting paradigms to enable better data annotation.
⬇️ Highlights below ⬇️
⚠️ We argue that dataset creators should consider annotator subjectivity in the annotation process and either explicitly encourage it or discourage it, depending on the intended use of their dataset ⚠️
As a framework, we propose two contrasting data annotation paradigms:
