Anna Sotnikova


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Which Examples Should be Multiply Annotated? Active Learning When Annotators May Disagree
Connor Baumler | Anna Sotnikova | Hal Daumé III
Findings of the Association for Computational Linguistics: ACL 2023

Linguistic annotations, especially for controversial topics like hate speech detection, are frequently contested due to annotator backgrounds and positionalities. In such situations, preserving this disagreement through the machine learning pipeline can be important for downstream use cases. However, capturing disagreement can increase annotation time and expense. Fortunately, for many tasks, not all examples are equally controversial; we develop an active learning approach, Disagreement Aware Active Learning (DAAL) that concentrates annotations on examples where model entropy and annotator entropy are the most different. Because we cannot know the true entropy of annotations on unlabeled examples, we estimate a model that predicts annotator entropy trained using very few multiply-labeled examples. We find that traditional uncertainty-based active learning underperforms simple passive learning on tasks with high levels of disagreement, but that our active learning approach is able to successfully improve on passive and active baselines, reducing the number of annotations required by at least 24% on average across several datasets.


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Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models
Yang Trista Cao | Anna Sotnikova | Hal Daumé III | Rachel Rudinger | Linda Zou
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale. We adapt the Agency-Belief-Communion (ABC) stereotype model of Koch et al. (2016) from social psychology as a framework for the systematic study and discovery of stereotypic group-trait associations in language models (LMs). We introduce the sensitivity test (SeT) for measuring stereotypical associations from language models. To evaluate SeT and other measures using the ABC model, we collect group-trait judgments from U.S.-based subjects to compare with English LM stereotypes. Finally, we extend this framework to measure LM stereotyping of intersectional identities.


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Analyzing Stereotypes in Generative Text Inference Tasks
Anna Sotnikova | Yang Trista Cao | Hal Daumé III | Rachel Rudinger
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021