@inproceedings{wang-etal-2021-textflint,
title = "{T}ext{F}lint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing",
author = "Wang, Xiao and
Liu, Qin and
Gui, Tao and
Zhang, Qi and
Zou, Yicheng and
Zhou, Xin and
Ye, Jiacheng and
Zhang, Yongxin and
Zheng, Rui and
Pang, Zexiong and
Wu, Qinzhuo and
Li, Zhengyan and
Zhang, Chong and
Ma, Ruotian and
Fei, Zichu and
Cai, Ruijian and
Zhao, Jun and
Hu, Xingwu and
Yan, Zhiheng and
Tan, Yiding and
Hu, Yuan and
Bian, Qiyuan and
Liu, Zhihua and
Qin, Shan and
Zhu, Bolin and
Xing, Xiaoyu and
Fu, Jinlan and
Zhang, Yue and
Peng, Minlong and
Zheng, Xiaoqing and
Zhou, Yaqian and
Wei, Zhongyu and
Qiu, Xipeng and
Huang, Xuanjing",
editor = "Ji, Heng and
Park, Jong C. and
Xia, Rui",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-demo.41/",
doi = "10.18653/v1/2021.acl-demo.41",
pages = "347--355",
abstract = "TextFlint is a multilingual robustness evaluation toolkit for NLP tasks that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. This enables practitioners to automatically evaluate their models from various aspects or to customize their evaluations as desired with just a few lines of code. TextFlint also generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model in terms of its robustness. To guarantee acceptability, all the text transformations are linguistically based and all the transformed data selected (up to 100,000 texts) scored highly under human evaluation. To validate the utility, we performed large-scale empirical evaluations (over 67,000) on state-of-the-art deep learning models, classic supervised methods, and real-world systems. The toolkit is already available at \url{https://github.com/textflint} with all the evaluation results demonstrated at textflint.io."
}
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<abstract>TextFlint is a multilingual robustness evaluation toolkit for NLP tasks that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. This enables practitioners to automatically evaluate their models from various aspects or to customize their evaluations as desired with just a few lines of code. TextFlint also generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model in terms of its robustness. To guarantee acceptability, all the text transformations are linguistically based and all the transformed data selected (up to 100,000 texts) scored highly under human evaluation. To validate the utility, we performed large-scale empirical evaluations (over 67,000) on state-of-the-art deep learning models, classic supervised methods, and real-world systems. The toolkit is already available at https://github.com/textflint with all the evaluation results demonstrated at textflint.io.</abstract>
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%0 Conference Proceedings
%T TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
%A Wang, Xiao
%A Liu, Qin
%A Gui, Tao
%A Zhang, Qi
%A Zou, Yicheng
%A Zhou, Xin
%A Ye, Jiacheng
%A Zhang, Yongxin
%A Zheng, Rui
%A Pang, Zexiong
%A Wu, Qinzhuo
%A Li, Zhengyan
%A Zhang, Chong
%A Ma, Ruotian
%A Fei, Zichu
%A Cai, Ruijian
%A Zhao, Jun
%A Hu, Xingwu
%A Yan, Zhiheng
%A Tan, Yiding
%A Hu, Yuan
%A Bian, Qiyuan
%A Liu, Zhihua
%A Qin, Shan
%A Zhu, Bolin
%A Xing, Xiaoyu
%A Fu, Jinlan
%A Zhang, Yue
%A Peng, Minlong
%A Zheng, Xiaoqing
%A Zhou, Yaqian
%A Wei, Zhongyu
%A Qiu, Xipeng
%A Huang, Xuanjing
%Y Ji, Heng
%Y Park, Jong C.
%Y Xia, Rui
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F wang-etal-2021-textflint
%X TextFlint is a multilingual robustness evaluation toolkit for NLP tasks that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. This enables practitioners to automatically evaluate their models from various aspects or to customize their evaluations as desired with just a few lines of code. TextFlint also generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model in terms of its robustness. To guarantee acceptability, all the text transformations are linguistically based and all the transformed data selected (up to 100,000 texts) scored highly under human evaluation. To validate the utility, we performed large-scale empirical evaluations (over 67,000) on state-of-the-art deep learning models, classic supervised methods, and real-world systems. The toolkit is already available at https://github.com/textflint with all the evaluation results demonstrated at textflint.io.
%R 10.18653/v1/2021.acl-demo.41
%U https://aclanthology.org/2021.acl-demo.41/
%U https://doi.org/10.18653/v1/2021.acl-demo.41
%P 347-355
Markdown (Informal)
[TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing](https://aclanthology.org/2021.acl-demo.41/) (Wang et al., ACL-IJCNLP 2021)
ACL
- Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, and Xuanjing Huang. 2021. TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 347–355, Online. Association for Computational Linguistics.