Robustness and Adversarial Examples in Natural Language Processing

Kai-Wei Chang, He He, Robin Jia, Sameer Singh

Abstract
Recent studies show that many NLP systems are sensitive and vulnerable to a small perturbation of inputs and do not generalize well across different datasets. This lack of robustness derails the use of NLP systems in real-world applications. This tutorial aims at bringing awareness of practical concerns about NLP robustness. It targets NLP researchers and practitioners who are interested in building reliable NLP systems. In particular, we will review recent studies on analyzing the weakness of NLP systems when facing adversarial inputs and data with a distribution shift. We will provide the audience with a holistic view of 1) how to use adversarial examples to examine the weakness of NLP models and facilitate debugging; 2) how to enhance the robustness of existing NLP models and defense against adversarial inputs; and 3) how the consideration of robustness affects the real-world NLP applications used in our daily lives. We will conclude the tutorial by outlining future research directions in this area.
Anthology ID:
2021.emnlp-tutorials.5
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic & Online
Editors:
Jing Jiang, Ivan Vulić
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
22–26
Language:
URL:
https://aclanthology.org/2021.emnlp-tutorials.5
DOI:
10.18653/v1/2021.emnlp-tutorials.5
Bibkey:
Cite (ACL):
Kai-Wei Chang, He He, Robin Jia, and Sameer Singh. 2021. Robustness and Adversarial Examples in Natural Language Processing. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, pages 22–26, Punta Cana, Dominican Republic & Online. Association for Computational Linguistics.
Cite (Informal):
Robustness and Adversarial Examples in Natural Language Processing (Chang et al., EMNLP 2021)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/2021.emnlp-tutorials.5.pdf
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