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Sparse Probability of Agreement

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arxiv 2208.06161 v2 pith:I6JANFPE submitted 2022-08-12 cs.CL

Jeppe N{o}rregaard , Leon Derczynski This is my paper · ORCID
classification cs.CL
keywords agreementprobabilityannotationdatasparseannotateannotator-item-pairsannotators
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Measuring inter-annotator agreement is important for annotation tasks, but many metrics require a fully-annotated set of data, where all annotators annotate all samples. We define Sparse Probability of Agreement, SPA, which estimates the probability of agreement when not all annotator-item-pairs are available. We show that under certain conditions, SPA is an unbiased estimator, and we provide multiple weighing schemes for handling data with various degrees of annotation.

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Cited by 1 Pith paper

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  1. QuMAB: Query-based Multi-Annotator Behavior Modeling with Reliability under Sparse Labels

    cs.MM 2025-07 conditional novelty 6.0 of 10

    QuMAB models each annotator with a lightweight query in a cross-attention network, reconstructs missing labels, and reports accuracy gains over aggregation baselines on two new dense-label datasets.

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