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Poster
in
Workshop: DMLR Workshop: Data-centric Machine Learning Research

Inter-Annotator Agreement in the Wild: Uncovering Its Emerging Roles and Considerations in Real-World Scenarios

NamHyeok Kim · Chanjun Park


Abstract:

Inter-Annotator Agreement (IAA) is commonly used as a measure of label consistency in natural language processing tasks. However, in real-world scenarios, IAA has various roles and implications beyond its traditional usage. In this paper, we not only consider IAA as a measure of consistency but also as a versatile tool that can be effectively utilized in practical applications. Moreover, we discuss various considerations and potential concerns when applying IAA and suggest strategies for effectively navigating these challenges.

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