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

TL;DR

This paper explores the evolving roles of Inter-Annotator Agreement in real-world NLP applications, highlighting its versatility, challenges, and strategies for effective use beyond traditional consistency measurement.

Contribution

It introduces a comprehensive perspective on IAA's practical applications and discusses considerations for its effective implementation in real-world scenarios.

Findings

IAA has multiple roles beyond measuring label consistency

Strategies for addressing challenges in applying IAA in practice

Guidelines for effective utilization of IAA in real-world NLP tasks

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.