Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets
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Mor Geva, Yoav Goldberg, Jonathan Berant. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
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Large performance disparities across users can indicate that models are exploiting biases or spurious correlations in the data.
“These large performance disparities across users are practical concerns in consumer-facing applications, and they can also indicate that models are exploiting biases or spurious correlations in the data (Badgeley et al., 2019; Geva et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Geva et al., 2019
Performance disparities across individuals have been observed in a wide range of tasks and applications, including natural language processing, automatic speech recognition, federated learning, and medical imaging.
“Performance disparities across individuals have been observed in a wide range of tasks and applications, including in natural language processing (Geva et al., 2019), automatic speech recognition (Koenecke et al., 2020; Tatman, 2017), federated learning (Li et al., 2019b; Caldas et al., 2018), and medical imaging (Badgeley et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Geva et al., 2019
Across a few natural language processing tasks where examples are annotated by crowdworkers, models have been observed to perform well on annotators commonly seen at training time, but fail to generalize to unseen annotators, suggesting that models are merely learning annotator-specific patterns and not the task.
“More directly, across a few natural language processing tasks where examples are annotated by crowdworkers, models have been observed to perform well on annotators that are commonly seen at training time, but fail to generalize to unseen annotators, suggesting that models are merely learning annotator-specific patterns and not the task (Geva et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Geva et al., 2019
Large performance disparities across users are practical concerns in consumer-facing applications and can indicate that models are exploiting biases or spurious correlations in the data.
“These large performance disparities across users are practical concerns in consumer-facing applications, and they can also indicate that models are exploiting biases or spurious correlations in the data (Badgeley et al., 2019; Geva et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Geva et al., 2019
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Digital History and Hermeneutics
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- Authors
- Geva, M., Goldberg, Y., & Berant, J.
- Affiliations
- Tel Aviv University (IL)
- Bar-Ilan University (IL)
- Venue
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Published
- 2019
- Pages
- 1161-1166
- OpenAlex
- W2969282158
- Cited by
- 284
- References
- 28
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 19 · 2023: 40 · 2022: 56 · 2021: 83 · 2020: 56 · 2019: 6 · 2018: 1
- FWCI
- 19.22
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Israel Science Foundation
- Tel Aviv University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- annotator bias, dataset construction, task modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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