Improving Robustness and Generality of NLP Models Using Disentangled
Representations

Improving Robustness and Generality of NLP Models Using Disentangled Representations

21 September 2020

Jiwei Li

Papers citing "Improving Robustness and Generality of NLP Models Using Disentangled Representations"

7 / 7 papers shown

Title
Disentangled Representation Learning Xin Eric Wang Hong Chen Siao Tang Zihao Wu Wenwu Zhu DRL 22 77 0 21 Nov 2022
A Mutual Information Maximization Perspective of Language Representation Learning Lingpeng Kong Cyprien de Masson dÁutume Wang Ling Lei Yu Zihang Dai Dani Yogatama SSL 212 165 0 18 Oct 2019
FreeLB: Enhanced Adversarial Training for Natural Language Understanding Chen Zhu Yu Cheng Zhe Gan S. Sun Tom Goldstein Jingjing Liu AAML 221 436 0 25 Sep 2019
Certified Robustness to Adversarial Word Substitutions Robin Jia Aditi Raghunathan Kerem Göksel Percy Liang AAML 178 290 0 03 Sep 2019
Design Challenges and Misconceptions in Neural Sequence Labeling Jie Yang Shuailong Liang Yue Zhang 118 161 0 12 Jun 2018
Generating Natural Language Adversarial Examples M. Alzantot Yash Sharma Ahmed Elgohary Bo-Jhang Ho Mani B. Srivastava Kai-Wei Chang AAML 245 914 0 21 Apr 2018
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks Mohit Iyyer John Wieting Kevin Gimpel Luke Zettlemoyer AAML GAN 185 711 0 17 Apr 2018