Inter-Annotator Agreement in the Wild: Uncovering Its Emerging Roles and Considerations in Real-World Scenarios
This work addresses the need for more versatile and practical applications of IAA in natural language processing, though it appears incremental as it builds on existing concepts without introducing a new method.
The paper tackles the problem of Inter-Annotator Agreement (IAA) being narrowly used as a measure of label consistency in NLP, uncovering its broader roles and implications in real-world scenarios, and suggests strategies for addressing associated challenges.
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.