@inproceedings{yu-etal-2022-measuring,
title = "Measuring Robustness for {NLP}",
author = "Yu, Yu and
Khan, Abdul Rafae and
Xu, Jia",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.343",
pages = "3908--3916",
abstract = "The quality of Natural Language Processing (NLP) models is typically measured by the accuracy or error rate of a predefined test set. Because the evaluation and optimization of these measures are narrowed down to a specific domain like news and cannot be generalized to other domains like Twitter, we often observe that a system reported with human parity results generates surprising errors in real-life use scenarios. We address this weakness with a new approach that uses an NLP quality measure based on robustness. Unlike previous work that has defined robustness using Minimax to bound worst cases, we measure robustness based on the consistency of cross-domain accuracy and introduce the coefficient of variation and (epsilon, gamma)-Robustness. Our measures demonstrate higher agreements with human evaluation than accuracy scores like BLEU on ranking Machine Translation (MT) systems. Our experiments of sentiment analysis and MT tasks show that incorporating our robustness measures into learning objectives significantly enhances the final NLP prediction accuracy over various domains, such as biomedical and social media.",
}
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%0 Conference Proceedings
%T Measuring Robustness for NLP
%A Yu, Yu
%A Khan, Abdul Rafae
%A Xu, Jia
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F yu-etal-2022-measuring
%X The quality of Natural Language Processing (NLP) models is typically measured by the accuracy or error rate of a predefined test set. Because the evaluation and optimization of these measures are narrowed down to a specific domain like news and cannot be generalized to other domains like Twitter, we often observe that a system reported with human parity results generates surprising errors in real-life use scenarios. We address this weakness with a new approach that uses an NLP quality measure based on robustness. Unlike previous work that has defined robustness using Minimax to bound worst cases, we measure robustness based on the consistency of cross-domain accuracy and introduce the coefficient of variation and (epsilon, gamma)-Robustness. Our measures demonstrate higher agreements with human evaluation than accuracy scores like BLEU on ranking Machine Translation (MT) systems. Our experiments of sentiment analysis and MT tasks show that incorporating our robustness measures into learning objectives significantly enhances the final NLP prediction accuracy over various domains, such as biomedical and social media.
%U https://aclanthology.org/2022.coling-1.343
%P 3908-3916
Markdown (Informal)
[Measuring Robustness for NLP](https://aclanthology.org/2022.coling-1.343) (Yu et al., COLING 2022)
ACL
- Yu Yu, Abdul Rafae Khan, and Jia Xu. 2022. Measuring Robustness for NLP. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3908–3916, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.