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SemEval-2021 Task 12: Learning with Disagreements
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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.
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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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Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning ...
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Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning ; Identificando el humor de tuits utilizando el aprendizaje de preferencias basado en procesos gaussianos
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Language Understanding in the Wild: Combining Crowdsourcing and Machine Learning
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Using a Bayesian Model to Combine LDA Features with Crowdsourced Responses
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In: DTIC (2013)
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