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Explore the complexities of inter-annotator agreement in error identification, particularly in human-human dialogs. Non-native speaker annotators, selected voluntarily from the lab, reveal challenges in pinpointing errors, with variations across datasets. The study highlights the impact of the modified Integrated Error Taxonomy in improving agreement, addressing challenges posed by specialized error types and introducing new categories for comprehensive coverage.

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