The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels
Eve Fleisig, Su Lin Blodgett, Dan Klein, Zeerak Talat
Abstract
Longstanding data labeling practices in machine learning involve collecting and aggregating labels from multiple annotators. But what should we do when annotators disagree? Though annotator disagreement has long been seen as a problem to minimize, new perspectivist approaches challenge this assumption by treating disagreement as a valuable source of information. In this position paper, we examine practices and assumptions surrounding the causes of disagreement–some challenged by perspectivist approaches, and some that remain to be addressed–as well as practical and normative challenges for work operating under these assumptions. We conclude with recommendations for the data labeling pipeline and avenues for future research engaging with subjectivity and disagreement.
BibTeX
@inproceedings{fleisig-etal-2024-perspectivist,
title = "The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels",
author = "Fleisig, Eve and
Blodgett, Su Lin and
Klein, Dan and
Talat, Zeerak",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.126/",
doi = "10.18653/v1/2024.naacl-long.126",
pages = "2279--2292"
}