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SemEval 2021 Task 12: Learning with Disagreement ...
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SemEval-2021 Task 12: Learning with Disagreements
Abstract: Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on learning with disagreements (Le-Wi-Di) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.
URL: http://repository.essex.ac.uk/31851/1/2021.semeval-1.41.pdf
http://repository.essex.ac.uk/31851/
https://doi.org/10.18653/v1/2021.semeval-1.41
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3
Making the Most of Crowd Information: Learning and Evaluation in AI tasks with Disagreements.
Uma., Alexandra Nnemamaka.. - : Queen Mary University of London., 2021
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We Need to Consider Disagreement in Evaluation
Basile, Valerio; Fell, Michael; Fornaciari, Tommaso. - : Association for Computational Linguistics, 2021. : country:USA, 2021. : place:Stroudsburg, PA, 2021
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A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
Paun, Silviu; Uma, Alexandra; Poesio, Massimo. - : Association for Computational Linguistics, 2019
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A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
Poesio, Massimo; Chamberlain, Jon; Paun, Silviu. - : Association for Computational Linguistics, 2019
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