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        <title>The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels
%A Fleisig, Eve
%A Blodgett, Su Lin
%A Klein, Dan
%A Talat, Zeerak
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F fleisig-etal-2024-perspectivist
%X 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.
%U https://aclanthology.org/2024.naacl-long.126
%P 2279-2292

Markdown (Informal)

[The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels](https://aclanthology.org/2024.naacl-long.126) (Fleisig et al., NAACL 2024)

• The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels (Fleisig et al., NAACL 2024)

ACL

• Eve Fleisig, Su Lin Blodgett, Dan Klein, and Zeerak Talat. 2024. The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 2279–2292, Mexico City, Mexico. Association for Computational Linguistics.