{"list":"other","total":51,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"2005.04118","title":"Beyond Accuracy: Behavioral Testing of NLP models with CheckList","created":"2020-05-08","cited_by_count":1006},{"id":"1703.04009","title":"Automated Hate Speech Detection and the Problem of Offensive Language","created":"2017-03-11","cited_by_count":736},{"id":"1908.07125","title":"Universal Adversarial Triggers for Attacking and Analyzing NLP","created":"2019-08-20","cited_by_count":722},{"id":"1506.04757","title":"Image-based Recommendations on Styles and Substitutes","created":"2015-06-15","cited_by_count":706},{"id":"1907.11932","title":"Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment","created":"2019-07-27","cited_by_count":671},{"id":"1707.07328","title":"Adversarial Examples for Evaluating Reading Comprehension Systems","created":"2017-07-23","cited_by_count":571},{"id":"1602.01585","title":"Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering","created":"2016-02-04","cited_by_count":520},{"id":"2004.09984","title":"BERT-ATTACK: Adversarial Attack Against BERT Using BERT","created":"2020-04-21","cited_by_count":491},{"id":"1804.07998","title":"Generating Natural Language Adversarial Examples","created":"2018-04-21","cited_by_count":424},{"id":"1801.04354","title":"Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers","created":"2018-01-13","cited_by_count":345},{"id":"2004.06660","title":"Weight Poisoning Attacks on Pre-trained Models","created":"2020-04-14","cited_by_count":341},{"id":"2106.07139","title":"Pre-Trained Models: Past, Present and Future","created":"2021-06-14","cited_by_count":319},{"id":"2004.01970","title":"BAE: BERT-based Adversarial Examples for Text Classification","created":"2020-04-04","cited_by_count":316},{"id":"2004.06100","title":"Pretrained Transformers Improve Out-of-Distribution Robustness","created":"2020-04-13","cited_by_count":294},{"id":"1804.06059","title":"Adversarial Example Generation with Syntactically Controlled Paraphrase Networks","created":"2018-04-17","cited_by_count":286},{"id":"1909.11764","title":"FreeLB: Enhanced Adversarial Training for Natural Language Understanding","created":"2019-09-25","cited_by_count":264},{"id":"1706.00188","title":"Deep Learning for Hate Speech Detection in Tweets","created":"2017-06-01","cited_by_count":180},{"id":"1909.00986","title":"Certified Robustness to Adversarial Word Substitutions","created":"2019-09-03","cited_by_count":168},{"id":"1905.11268","title":"Combating Adversarial Misspellings with Robust Word Recognition","created":"2019-05-27","cited_by_count":163},{"id":"2110.07139","title":"Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer","created":"2021-10-14","cited_by_count":158},{"id":"1801.07883","title":"Deep Learning for Sentiment Analysis : A Survey","created":"2018-01-24","cited_by_count":139},{"id":"2009.07502","title":"Contextualized Perturbation for Textual Adversarial Attack","created":"2020-09-16","cited_by_count":136},{"id":"2101.04840","title":"Robustness Gym: Unifying the NLP Evaluation Landscape","created":"2021-01-13","cited_by_count":108},{"id":"1604.08275","title":"Crafting Adversarial Input Sequences for Recurrent Neural Networks","created":"2016-04-28","cited_by_count":103},{"id":"1909.01492","title":"Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation","created":"2019-09-03","cited_by_count":88},{"id":"1806.09030","title":"On Adversarial Examples for Character-Level Neural Machine Translation","created":"2018-06-23","cited_by_count":85},{"id":"2009.09191","title":"OpenAttack: An Open-source Textual Adversarial Attack Toolkit","created":"2020-09-19","cited_by_count":84},{"id":"2109.06827","title":"Types of Out-of-Distribution Texts and How to Detect Them","created":"2021-09-14","cited_by_count":80},{"id":"2206.08514","title":"A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks","created":"2022-06-17","cited_by_count":74},{"id":"2005.14424","title":"SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions","created":"2020-05-29","cited_by_count":73},{"id":"2005.01229","title":"Robust Encodings: A Framework for Combating Adversarial Typos","created":"2020-05-04","cited_by_count":58},{"id":"1909.03084","title":"Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification","created":"2019-09-06","cited_by_count":57},{"id":"2204.05239","title":"Exploring the Universal Vulnerability of Prompt-based Learning Paradigm","created":"2022-04-11","cited_by_count":57},{"id":"1707.02812","title":"Towards Crafting Text Adversarial Samples","created":"2017-07-10","cited_by_count":55},{"id":"2004.05887","title":"Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples","created":"2020-04-13","cited_by_count":54},{"id":"1902.01509","title":"Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation","created":"2019-02-05","cited_by_count":52},{"id":"1806.05599","title":"A Survey on Open Information Extraction","created":"2018-06-14","cited_by_count":51},{"id":"2012.14956","title":"Generating Natural Language Attacks in a Hard Label Black Box Setting","created":"2020-12-29","cited_by_count":50},{"id":"2010.02338","title":"CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation","created":"2020-10-05","cited_by_count":44},{"id":"1909.06723","title":"Natural Language Adversarial Defense through Synonym Encoding","created":"2019-09-15","cited_by_count":40},{"id":"2012.15699","title":"Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning","created":"2020-12-31","cited_by_count":39},{"id":"2105.14553","title":"Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice","created":"2021-05-30","cited_by_count":30},{"id":"2110.15317","title":"Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework","created":"2021-10-28","cited_by_count":28},{"id":"2104.05062","title":"Achieving Model Robustness through Discrete Adversarial Training","created":"2021-04-11","cited_by_count":23},{"id":"2004.14870","title":"Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding","created":"2020-04-30","cited_by_count":22},{"id":"2109.04367","title":"Multi-granularity Textual Adversarial Attack with Behavior Cloning","created":"2021-09-09","cited_by_count":21},{"id":"2110.08247","title":"Textual Backdoor Attacks Can Be More Harmful via Two Simple Tricks","created":"2021-10-15","cited_by_count":18},{"id":"1709.01189","title":"Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features","created":"2017-09-04","cited_by_count":14},{"id":"2009.09192","title":"Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations","created":"2020-09-19","cited_by_count":7}],"next_cursor":null}