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.
@article{arxiv.2404.15720,
title = {Annotator-Centric Active Learning for Subjective NLP Tasks},
author = {Michiel van der Meer and Neele Falk and Pradeep K. Murukannaiah and Enrico Liscio},
journal= {arXiv preprint arXiv:2404.15720},
year = {2024}
}