A curated list of awesome datasets with human label variation (un-aggregated labels) in Natural Language Processing and Computer Vision, accompanying The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation (EMNLP 2022)
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Suggestion for Relevant Work on Your Repository #5
This paper takes a unique look at label variation by studying the impact of annotators' demographics on their disagreements in NLP tasks. Our findings reveal that demographic information, such as gender, ethnicity, and education level, is instrumental in predicting annotator disagreements.
Hello,
I'm reaching out to express my appreciation for the valuable open-source repository you've organized!
In line with your project's theme, I'd like to suggest adding my recent AAAI paper, "Everyone's Voice Matters: Quantifying Annotation Disagreement Using Demographic Information" to the key references of your GitHub repository.
This paper takes a unique look at label variation by studying the impact of annotators' demographics on their disagreements in NLP tasks. Our findings reveal that demographic information, such as gender, ethnicity, and education level, is instrumental in predicting annotator disagreements.
I look forward to hearing from you.
Best regards, Ruyuan