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        <title>Improving Label Quality by Jointly Modeling Items and Annotators</title>
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        <namePart type="given">Tharindu</namePart>
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    <abstract>We propose a fully Bayesian framework for learning ground truth labels from noisy annotators. Our framework ensures scalability by factoring a generative, Bayesian soft clustering model over label distributions into the classic David and Skene joint annotator-data model. Earlier research along these lines has neither fully incorporated label distributions nor explored clustering by annotators only or data only. Our framework incorporates all of these properties within a graphical model designed to provide better ground truth estimates of annotator responses as input to any black box supervised learning algorithm. We conduct supervised learning experiments with variations of our models and compare them to the performance of several baseline models.</abstract>
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%0 Conference Proceedings
%T Improving Label Quality by Jointly Modeling Items and Annotators
%A Weerasooriya, Tharindu Cyril
%A Ororbia, Alexander
%A Homan, Christopher
%Y Abercrombie, Gavin
%Y Basile, Valerio
%Y Tonelli, Sara
%Y Rieser, Verena
%Y Uma, Alexandra
%S Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F weerasooriya-etal-2022-improving
%X We propose a fully Bayesian framework for learning ground truth labels from noisy annotators. Our framework ensures scalability by factoring a generative, Bayesian soft clustering model over label distributions into the classic David and Skene joint annotator-data model. Earlier research along these lines has neither fully incorporated label distributions nor explored clustering by annotators only or data only. Our framework incorporates all of these properties within a graphical model designed to provide better ground truth estimates of annotator responses as input to any black box supervised learning algorithm. We conduct supervised learning experiments with variations of our models and compare them to the performance of several baseline models.
%U https://aclanthology.org/2022.nlperspectives-1.12
%P 95-99

Markdown (Informal)

[Improving Label Quality by Jointly Modeling Items and Annotators](https://aclanthology.org/2022.nlperspectives-1.12) (Weerasooriya et al., NLPerspectives 2022)

• Improving Label Quality by Jointly Modeling Items and Annotators (Weerasooriya et al., NLPerspectives 2022)

ACL

• Tharindu Cyril Weerasooriya, Alexander Ororbia, and Christopher Homan. 2022. Improving Label Quality by Jointly Modeling Items and Annotators. In Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022, pages 95–99, Marseille, France. European Language Resources Association.