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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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Capturing Ambiguity in Crowdsourcing Frame Disambiguation ...
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Capturing Ambiguity in Crowdsourcing Frame Disambiguation
In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing; Vol. 6 No. 1 (2018): Sixth AAAI Conference on Human Computation and Crowdsourcing (2018)
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