Contributed by Chenghao Yang, Fanchao Qi and Yuan Zang.

1. Analysis Methods in Neural Language Processing: A Survey. Yonatan Belinkov, James Glass. TACL 2019. [pdf]
2. Towards a Robust Deep Neural Network in Text Domain A Survey. Wenqi Wang, Lina Wang, Benxiao Tang, Run Wang, Aoshuang Ye. 2019. [pdf]
3. Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey. Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, Chenliang Li. 2019. [pdf]

1. PAWS: Paraphrase Adversaries from Word Scrambling. Yuan Zhang, Jason Baldridge, Luheng He. NAACL-HLT 2019. [pdf] [dataset]
2. Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems. Steffen Eger, Gözde Gül ¸Sahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych. NAACL-HLT 2019. [pdf]
3. Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models. Tong Niu, Mohit Bansal. CoNLL 2018. [pdf]
4. Generating Natural Language Adversarial Examples. Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, Kai-Wei Chang. EMNLP 2018. [pdf]
5. Breaking NLI Systems with Sentences that Require Simple Lexical Inferences. Max Glockner, Vered Shwartz, Yoav Goldberg ACL 2018. [pdf]
6. AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples. Dongyeop Kang, Tushar Khot, Ashish Sabharwal, Eduard Hovy. ACL 2018. [pdf]
7. Semantically Equivalent Adversarial Rules for Debugging NLP Models. Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin ACL 2018. [pdf]
8. Robust Machine Comprehension Models via Adversarial Training. Yicheng Wang, Mohit Bansal. NAACL-HLT 2018. [pdf]
9. Adversarial Example Generation with Syntactically Controlled Paraphrase Networks. Mohit Iyyer, John Wieting, Kevin Gimpel, Luke Zettlemoyer. NAACL-HLT 2018. [pdf]
10. Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers. Ji Gao, Jack Lanchantin, Mary Lou Soffa, Yanjun Qi. IEEE SPW 2018. [pdf]
11. Synthetic and Natural Noise Both Break Neural Machine Translation. Yonatan Belinkov, Yonatan Bisk. ICLR 2018. [pdf]
12. Generating Natural Adversarial Examples. Zhengli Zhao, Dheeru Dua, Sameer Singh. ICLR 2018. [pdf]
13. Adversarial Examples for Evaluating Reading Comprehension Systems. Robin Jia, and Percy Liang. EMNLP 2017. [pdf]

1. On Adversarial Examples for Character-Level Neural Machine Translation. Javid Ebrahimi, Daniel Lowd, Dejing Dou. COLING 2018. [pdf]
2. HotFlip: White-Box Adversarial Examples for Text Classification. Javid Ebrahimi, Anyi Rao, Daniel Lowd, Dejing Dou. ACL 2018. [pdf]
3. Towards Crafting Text Adversarial Samples. Suranjana Samanta, Sameep Mehta. ECIR 2018. [pdf]

1. TEXTBUGGER: Generating Adversarial Text Against Real-world Applications. Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, Ting Wang. NDSS 2019. [pdf]
2. Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension. Matthias Blohm, Glorianna Jagfeld, Ekta Sood, Xiang Yu, Ngoc Thang Vu. CoNLL 2018. [pdf]
3. Deep Text Classification Can be Fooled. Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi. IJCAI 2018. [pdf]

1. Combating Adversarial Misspellings with Robust Word Recognition. Danish Pruthi, Bhuwan Dhingra, Zachary C. Lipton. ACL 2019. [pdf]

1. On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models. Paul Michel, Xian Li, Graham Neubig, Juan Miguel Pino. NAACL-HLT 2019. [pdf]

1. Unified Visual-Semantic Embeddings: Bridging Vision and Language with Structured Meaning Representations. Hao Wu, Jiayuan Mao, Yufeng Zhang, Yuning Jiang, Lei Li, Weiwei Sun, Wei-Ying Ma. CVPR 2019. [pdf]
2. Learning Visually-Grounded Semantics from Contrastive Adversarial Samples. Haoyue Shi, Jiayuan Mao, Tete Xiao, Yuning Jiang, Jian Sun. COLING 2018. [pdf]