Boundaries Agree, Labels Do Not: Intra-Annotator Dynamics as a Kind of Training Data

Data quality now matters as much as compute for training language models. Much training data comes from human annotation of text, and interpretive annotation has no ground truth that could settle what is "accurate". Two lines of work respond to this.