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Many machine learning tasks -- particularly those in affective computing -- are inherently subjective.
A coefficient of agreement for nominal scales
Cohen, J. 1960 · 1960
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A circumplex model of affect
Russell, J. A. 1980 · 1980
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Bagging predictors
Breiman, L. 1996 · 1996
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Subjective natural language problems: Motivations, applications, characterizations, and implications
Alm, C. O. 2011 · 2011
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R.; Perelygin, A.; Wu, J.; Chuang, J.; Manning, C. D.; Ng, A.; and Potts, C. 2013 · 2013
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Label distribution learning
Geng, X. 2016 · 2016
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Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages
Zadeh, A.; Zellers, R.; Pincus, E.; and Morency, L.-P. 2016 · 2016
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From Hard to Soft: Towards More Human-like Emotion Recognition by Modelling the Perception Uncertainty
Han, J.; Zhang, Z.; Schmitt, M.; Pantic, M.; and Schuller, B. 2017 · 2017
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Predicting the Distribution of Emotion Perception: Capturing Inter-Rater Variability
Zhang, B.; Essl, G.; and Mower Provost, E. 2017 · 2017
Cited alongside, same era.
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph
Bagher Zadeh, A.; Liang, P. P.; Poria, S.; Cambria, E.; and Morency, L.-P. 2018 · 2018
Cited alongside, same era.
CARER: Contextualized Affect Representations for Emotion Recognition
Saravia, E.; Liu, H.-C. T.; Huang, Y.-H.; Wu, J.; and Chen, Y.-S. 2018 · 2018
Cited alongside, same era.
The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality
Gordon, M. L.; Zhou, K.; Patel, K.; Hashimoto, T.; and Bernstein, M. S. 2021 · 2021
Later among the works it cites.
Learning personal human biases and representations for subjective tasks in natural language processing
Kocoń, J.; Gruza, M.; Bielaniewicz, J.; Grimling, D.; Kanclerz, K.; Miłkowski, P.; and Kazienko, P. 2021 · 2021
Later among the works it cites.
Dealing with disagreements: Looking beyond the majority vote in subjective annotations
Davani, A. M.; Díaz, M.; and Prabhakaran, V. 2022 · 2022
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Jury learning: Integrating dissenting voices into machine learning models
Gordon, M. L.; Lam, M. S.; Park, J. S.; Patel, K.; Hancock, J.; Hashimoto, T.; and Bernstein, M. S. 2022 · 2022
Closest in time.
End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks
Raj Prabhu, N.; Carbajal, G.; Lehmann-Willenbrock, N.; and Gerkmann, T. 2022 · 2022
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Kenton, J. D. M.-W. C.; and Toutanova, L. K. 2019 · 2019
Cited alongside, same era.