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This paper introduces a methodological framework for decomposing annotation tasks to better analyze inter-annotator disagreement, which is critical for improving the reliability of training data used in machine learning models. By distinguishing between theoretically-driven and exploration-based decomposition strategies, the work offers researchers tools to isolate sources of error and reveal latent structures within complex labeling datasets. While the study focuses on natural language processing applications, its implications extend to enhancing data quality across broader AI development pipelines.
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A Decomposition-Based Approach for Evaluating and Analyzing Inter-Annotator Disagreement

arXiv2 min read

arXiv:2206.05446v3 Announce Type: replace Abstract: We propose a novel method to conceptually decompose an existing annotation into separate levels, allowing the analysis of inter-annotators disagreement in each level separately. We suggest two distinct strategies in order to actualize this approach: a theoretically-driven one, in which the researcher defines a decomposition based on prior knowledge of the annotation task, and an exploration-based one, in which many possible decompositions are inductively computed and presented to the researcher for interpretation and evaluation. Utilizing a recently constructed dataset for narrative analysis as our use-case, we apply each of the two strategies to demonstrate the potential of our approach in testing hypotheses regarding the sources of annotation disagreements, as well as revealing latent structures and relations within the annotation task. We conclude by suggesting how to extend and generalize our approach, as well as use it for other purposes.

Abstract:We propose a novel method to conceptually decompose an existing annotation into separate levels, allowing the analysis of inter-annotators disagreement in each level separately. We suggest two distinct strategies in order to actualize this approach: a theoretically-driven one, in which the researcher defines a decomposition based on prior knowledge of the annotation task, and an exploration-based one, in which many possible decompositions are inductively computed and presented to the researcher for interpretation and evaluation. Utilizing a recently constructed dataset for narrative analysis as our use-case, we apply each of the two strategies to demonstrate the potential of our approach in testing hypotheses regarding the sources of annotation disagreements, as well as revealing latent structures and relations within the annotation task. We conclude by suggesting how to extend and generalize our approach, as well as use it for other purposes.

Computation and Language (cs.CL)

arXiv:2206.05446 [cs.CL]

(or arXiv:2206.05446v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2206.05446 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 232 words

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