Papers with Cohen’s Kappa

5 papers
A Multi-Axis Annotation Scheme for Event Temporal Relations (P18-1)

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Challenge: Existing temporal relation (TempRel) annotation schemes have low inter-annotator agreements even between experts, suggesting that the current annotation task needs a better definition.
Approach: They propose to annotate temporal relation (TempRel) annotation schemes based on event start-points instead of a conventional 60’s-80’s model.
Outcome: The proposed model improves IAA from the conventional 60’s to 80’s and can be used by crowdsourcing to alleviate labor intensity.
ROSE: An Intent-Centered Evaluation Metric for NL2SQL (2026.acl-long)

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Challenge: Existing evaluation metrics for evaluating the effectiveness of Natural Language to SQL (NL2SQL) solutions are becoming unreliable due to its sensitiveness to syntactic variation and inconsistent consistency with ground-truth SQL.
Approach: They propose an intent-centered metric that focuses on whether the predicted SQL answers the question, rather than consistency with the ground-truth SQL.
Outcome: The proposed metric outperforms the next-best metric by nearly 24% on the expert-aligned validation set **ROSE-VEC**.
Establishing Annotation Quality in Multi-label Annotations (2022.coling-1)

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Challenge: Multi-label annotations allow multiple interpretations of a single item, but they also affect the chance that two coders agree with each other.
Approach: They propose a bootstrapped method to obtain chance agreement for each measure and a method to get an adjusted agreement coefficient that is more interpretable.
Outcome: The proposed method allows for an adjusted agreement coefficient that is more interpretable on simulated datasets.
Automating Idea Unit Segmentation and Alignment for Assessing Reading Comprehension via Summary Protocol Analysis (2022.lrec-1)

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Challenge: In second language learning, summaries are among the most popular type of student assignments.
Approach: They propose to revise the annotation guidelines to allow machine implementation of the new annotation guidelines.
Outcome: The proposed algorithm achieves 0.789 precision and 0.844 recall over the L2WS 2021 corpus.
Common Law Annotations: Investigating the Stability of Dialog System Output Annotations (2023.findings-acl)

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Challenge: High agreement is often used to show reliability of annotation procedures, but it is insufficient to ensure or reproducibility.
Approach: They propose a protocol that increases Inter-Annotator Agreement among annotators and a standardized and codified protocol that strictly enforces transparency in the annotation process.
Outcome: The proposed protocol ensures transparency in the annotation process, which ensures reproducibility of annotation guidelines.

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