@inproceedings{stengel-eskin-etal-2021-human,
title = "Human-Model Divergence in the Handling of Vagueness",
author = "Stengel-Eskin, Elias and
Guallar-Blasco, Jimena and
Van Durme, Benjamin",
editor = "Roth, Michael and
Tsarfaty, Reut and
Goldberg, Yoav",
booktitle = "Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.unimplicit-1.6",
doi = "10.18653/v1/2021.unimplicit-1.6",
pages = "43--57",
abstract = "While aggregate performance metrics can generate valuable insights at a large scale, their dominance means more complex and nuanced language phenomena, such as vagueness, may be overlooked. Focusing on vague terms (e.g. sunny, cloudy, young, etc.) we inspect the behavior of visually grounded and text-only models, finding systematic divergences from human judgments even when a model{'}s overall performance is high. To help explain this disparity, we identify two assumptions made by the datasets and models examined and, guided by the philosophy of vagueness, isolate cases where they do not hold.",
}
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<abstract>While aggregate performance metrics can generate valuable insights at a large scale, their dominance means more complex and nuanced language phenomena, such as vagueness, may be overlooked. Focusing on vague terms (e.g. sunny, cloudy, young, etc.) we inspect the behavior of visually grounded and text-only models, finding systematic divergences from human judgments even when a model’s overall performance is high. To help explain this disparity, we identify two assumptions made by the datasets and models examined and, guided by the philosophy of vagueness, isolate cases where they do not hold.</abstract>
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%0 Conference Proceedings
%T Human-Model Divergence in the Handling of Vagueness
%A Stengel-Eskin, Elias
%A Guallar-Blasco, Jimena
%A Van Durme, Benjamin
%Y Roth, Michael
%Y Tsarfaty, Reut
%Y Goldberg, Yoav
%S Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F stengel-eskin-etal-2021-human
%X While aggregate performance metrics can generate valuable insights at a large scale, their dominance means more complex and nuanced language phenomena, such as vagueness, may be overlooked. Focusing on vague terms (e.g. sunny, cloudy, young, etc.) we inspect the behavior of visually grounded and text-only models, finding systematic divergences from human judgments even when a model’s overall performance is high. To help explain this disparity, we identify two assumptions made by the datasets and models examined and, guided by the philosophy of vagueness, isolate cases where they do not hold.
%R 10.18653/v1/2021.unimplicit-1.6
%U https://aclanthology.org/2021.unimplicit-1.6
%U https://doi.org/10.18653/v1/2021.unimplicit-1.6
%P 43-57
Markdown (Informal)
[Human-Model Divergence in the Handling of Vagueness](https://aclanthology.org/2021.unimplicit-1.6) (Stengel-Eskin et al., unimplicit 2021)
ACL
- Elias Stengel-Eskin, Jimena Guallar-Blasco, and Benjamin Van Durme. 2021. Human-Model Divergence in the Handling of Vagueness. In Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language, pages 43–57, Online. Association for Computational Linguistics.