A meta-learned embedding with an EM-adapted Gaussian mixture and annotator confusion matrices improves few-shot classification from multiple noisy annotators.
Boldface denotes the best and comparable methods according to the paired t-test (p= 0.05)
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Meta-learning Representations for Learning from Multiple Annotators
A meta-learned embedding with an EM-adapted Gaussian mixture and annotator confusion matrices improves few-shot classification from multiple noisy annotators.