Duallabeled

Duallabeled is a term used to describe data or annotation processes in which each data instance carries two labels. These dual labels are typically produced independently by two annotators or by applying two labeling schemes to the same item. The goal of duallabeled data is to improve label reliability, capture ambiguity, or support more nuanced modeling than single-label annotations.

In practice, duallabeled annotation often follows parallel labeling, where two annotators work independently, or cross-labeling, where one label is derived from one method and a second label from another. Annotations are usually tracked with annotator identifiers and may include confidence scores. After labeling, the two labels can be reconciled through adjudication, majority voting, or probabilistic fusion to create a final or soft label set. The existence of dual labels also enables measurement of inter-annotator agreement and estimation of labeling uncertainty.

Duallabeled data finds use across domains such as natural language processing, computer vision, speech recognition, and medical imaging. It is especially valuable when tasks involve subjectivity, nuanced distinctions, or high-stakes decisions, where two independent perspectives help quantify reliability or capture multiple valid interpretations. Limitations include higher labeling cost and time, potential systematic disagreements, and the need for clear reconciliation strategies or models that can leverage dual-label information rather than collapsing it prematurely.