Annotator-Centric Active Learning for Subjective NLP Tasks
M.T. van der Meer (Idiap Research Institute, Universiteit Leiden)
Neele Falk (University of Stuttgart)
Pradeep Murukannaiah (TU Delft - Interactive Intelligence)
E. Liscio (TU Delft - Interactive Intelligence)
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Abstract
Active Learning (AL) addresses the high costs of collecting human annotations by strategically annotating the most informative samples.However, for subjective NLP tasks, incorporating a wide range of perspectives in the annotation process is crucial to capture the variability in human judgments.We introduce Annotator-Centric Active Learning (ACAL), which incorporates an annotator selection strategy following data sampling.Our objective is two-fold: (1) to efficiently approximate the full diversity of human judgments, and (2) to assess model performance using annotator-centric metrics, which value minority and majority perspectives equally.We experiment with multiple annotator selection strategies across seven subjective NLP tasks, employing both traditional and novel, human-centered evaluation metrics.Our findings indicate that ACAL improves data efficiency and excels in annotator-centric performance evaluations.However, its success depends on the availability of a sufficiently large and diverse pool of annotators to sample from.
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