@inproceedings{gardner-etal-2020-evaluating,
title = "Evaluating Models' Local Decision Boundaries via Contrast Sets",
author = "Gardner, Matt and
Artzi, Yoav and
Basmov, Victoria and
Berant, Jonathan and
Bogin, Ben and
Chen, Sihao and
Dasigi, Pradeep and
Dua, Dheeru and
Elazar, Yanai and
Gottumukkala, Ananth and
Gupta, Nitish and
Hajishirzi, Hannaneh and
Ilharco, Gabriel and
Khashabi, Daniel and
Lin, Kevin and
Liu, Jiangming and
Liu, Nelson F. and
Mulcaire, Phoebe and
Ning, Qiang and
Singh, Sameer and
Smith, Noah A. and
Subramanian, Sanjay and
Tsarfaty, Reut and
Wallace, Eric and
Zhang, Ally and
Zhou, Ben",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.117/",
doi = "10.18653/v1/2020.findings-emnlp.117",
pages = "1307--1323",
abstract = "Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture the abilities a dataset is intended to test. We propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. In particular, after a dataset is constructed, we recommend that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Contrast sets provide a local view of a model`s decision boundary, which can be used to more accurately evaluate a model`s true linguistic capabilities. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets (e.g., DROP reading comprehension, UD parsing, and IMDb sentiment analysis). Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets{---}up to 25{\%} in some cases. We release our contrast sets as new evaluation benchmarks and encourage future dataset construction efforts to follow similar annotation processes."
}
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<abstract>Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture the abilities a dataset is intended to test. We propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. In particular, after a dataset is constructed, we recommend that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Contrast sets provide a local view of a model‘s decision boundary, which can be used to more accurately evaluate a model‘s true linguistic capabilities. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets (e.g., DROP reading comprehension, UD parsing, and IMDb sentiment analysis). Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets—up to 25% in some cases. We release our contrast sets as new evaluation benchmarks and encourage future dataset construction efforts to follow similar annotation processes.</abstract>
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%0 Conference Proceedings
%T Evaluating Models’ Local Decision Boundaries via Contrast Sets
%A Gardner, Matt
%A Artzi, Yoav
%A Basmov, Victoria
%A Berant, Jonathan
%A Bogin, Ben
%A Chen, Sihao
%A Dasigi, Pradeep
%A Dua, Dheeru
%A Elazar, Yanai
%A Gottumukkala, Ananth
%A Gupta, Nitish
%A Hajishirzi, Hannaneh
%A Ilharco, Gabriel
%A Khashabi, Daniel
%A Lin, Kevin
%A Liu, Jiangming
%A Liu, Nelson F.
%A Mulcaire, Phoebe
%A Ning, Qiang
%A Singh, Sameer
%A Smith, Noah A.
%A Subramanian, Sanjay
%A Tsarfaty, Reut
%A Wallace, Eric
%A Zhang, Ally
%A Zhou, Ben
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F gardner-etal-2020-evaluating
%X Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture the abilities a dataset is intended to test. We propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. In particular, after a dataset is constructed, we recommend that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Contrast sets provide a local view of a model‘s decision boundary, which can be used to more accurately evaluate a model‘s true linguistic capabilities. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets (e.g., DROP reading comprehension, UD parsing, and IMDb sentiment analysis). Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets—up to 25% in some cases. We release our contrast sets as new evaluation benchmarks and encourage future dataset construction efforts to follow similar annotation processes.
%R 10.18653/v1/2020.findings-emnlp.117
%U https://aclanthology.org/2020.findings-emnlp.117/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.117
%P 1307-1323
Markdown (Informal)
[Evaluating Models’ Local Decision Boundaries via Contrast Sets](https://aclanthology.org/2020.findings-emnlp.117/) (Gardner et al., Findings 2020)
ACL
- Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou. 2020. Evaluating Models’ Local Decision Boundaries via Contrast Sets. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1307–1323, Online. Association for Computational Linguistics.