{"list":"other","total":116,"rows":[{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1606.05250","title":"SQuAD: 100,000+ Questions for Machine Comprehension of Text","created":"2016-06-16","cited_by_count":4148},{"id":"1808.08745","title":"Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization","created":"2018-08-27","cited_by_count":1317},{"id":"1802.00420","title":"Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples","created":"2018-02-01","cited_by_count":1268},{"id":"1506.03340","title":"Teaching Machines to Read and Comprehend","created":"2015-06-10","cited_by_count":1049},{"id":"1704.03976","title":"Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning","created":"2017-04-13","cited_by_count":920},{"id":"1910.14599","title":"Adversarial NLI: A New Benchmark for Natural Language Understanding","created":"2019-10-31","cited_by_count":807},{"id":"1710.03957","title":"DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset","created":"2017-10-11","cited_by_count":652},{"id":"1904.12843","title":"Adversarial Training for Free!","created":"2019-04-29","cited_by_count":643},{"id":"1707.07328","title":"Adversarial Examples for Evaluating Reading Comprehension Systems","created":"2017-07-23","cited_by_count":571},{"id":"1802.03471","title":"Certified Robustness to Adversarial Examples with Differential Privacy","created":"2018-02-09","cited_by_count":468},{"id":"1801.00553","title":"Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey","created":"2018-01-02","cited_by_count":460},{"id":"1804.07998","title":"Generating Natural Language Adversarial Examples","created":"2018-04-21","cited_by_count":424},{"id":"1904.01130","title":"PAWS: Paraphrase Adversaries from Word Scrambling","created":"2019-04-01","cited_by_count":402},{"id":"1812.05271","title":"TextBugger: Generating Adversarial Text Against Real-world Applications","created":"2018-12-13","cited_by_count":381},{"id":"1712.07107","title":"Adversarial Examples: Attacks and Defenses for Deep Learning","created":"2017-12-19","cited_by_count":351},{"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":"1810.12715","title":"On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models","created":"2018-10-30","cited_by_count":328},{"id":"2004.01970","title":"BAE: BERT-based Adversarial Examples for Text Classification","created":"2020-04-04","cited_by_count":316},{"id":"1711.02173","title":"Synthetic and Natural Noise Both Break Neural Machine Translation","created":"2017-11-06","cited_by_count":306},{"id":"1801.09344","title":"Certified Defenses against Adversarial Examples","created":"2018-01-29","cited_by_count":306},{"id":"1706.03691","title":"Certified Defenses for Data Poisoning Attacks","created":"2017-06-09","cited_by_count":303},{"id":"1804.06059","title":"Adversarial Example Generation with Syntactically Controlled Paraphrase Networks","created":"2018-04-17","cited_by_count":286},{"id":"1812.09449","title":"A Survey on Deep Learning for Named Entity Recognition","created":"2018-12-22","cited_by_count":282},{"id":"1605.07725","title":"Adversarial Training Methods for Semi-Supervised Text Classification","created":"2016-05-25","cited_by_count":280},{"id":"1909.11764","title":"FreeLB: Enhanced Adversarial Training for Natural Language Understanding","created":"2019-09-25","cited_by_count":264},{"id":"1810.00069","title":"Adversarial Attacks and Defences: A Survey","created":"2018-09-28","cited_by_count":259},{"id":"1909.08072","title":"Adversarial Attacks and Defenses in Images, Graphs and Text: A Review","created":"2019-09-17","cited_by_count":237},{"id":"1710.11342","title":"Generating Natural Adversarial Examples","created":"2017-10-31","cited_by_count":183},{"id":"1801.10578","title":"Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach","created":"2018-01-31","cited_by_count":182},{"id":"1908.06083","title":"Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack","created":"2019-08-17","cited_by_count":176},{"id":"1909.00986","title":"Certified Robustness to Adversarial Word Substitutions","created":"2019-09-03","cited_by_count":168},{"id":"1904.08067","title":"Text Classification Algorithms: A Survey","created":"2019-04-17","cited_by_count":163},{"id":"1905.11268","title":"Combating Adversarial Misspellings with Robust Word Recognition","created":"2019-05-27","cited_by_count":163},{"id":"2104.06683","title":"The Curious Case of Hallucinations in Neural Machine Translation","created":"2021-04-14","cited_by_count":161},{"id":"1604.04326","title":"Improving the Robustness of Deep Neural Networks via Stability Training","created":"2016-04-15","cited_by_count":156},{"id":"1907.10456","title":"Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems","created":"2019-07-24","cited_by_count":153},{"id":"1603.00892","title":"Counter-fitting Word Vectors to Linguistic Constraints","created":"2016-03-02","cited_by_count":140},{"id":"2104.05596","title":"Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages","created":"2021-04-12","cited_by_count":136},{"id":"1910.11470","title":"A Survey on Recent Advances in Named Entity Recognition from Deep Learning models","created":"2019-10-25","cited_by_count":117},{"id":"1708.05148","title":"Natural Language Processing: State of The Art, Current Trends and Challenges","created":"2017-08-17","cited_by_count":114},{"id":"2001.02378","title":"MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius","created":"2020-01-08","cited_by_count":114},{"id":"2004.08994","title":"Adversarial Training for Large Neural Language Models","created":"2020-04-20","cited_by_count":111},{"id":"1704.08006","title":"Deep Text Classification Can be Fooled","created":"2017-04-26","cited_by_count":106},{"id":"1906.02443","title":"Robust Neural Machine Translation with Doubly Adversarial Inputs","created":"2019-06-06","cited_by_count":106},{"id":"1702.08138","title":"Deceiving Google's Perspective API Built for Detecting Toxic Comments","created":"2017-02-27","cited_by_count":102},{"id":"1911.12116","title":"Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey","created":"2019-11-27","cited_by_count":93},{"id":"2010.02329","title":"InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective","created":"2020-10-05","cited_by_count":92},{"id":"1704.05742","title":"Adversarial Multi-task Learning for Text Classification","created":"2017-04-19","cited_by_count":91},{"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":"2004.15015","title":"Imitation Attacks and Defenses for Black-box Machine Translation Systems","created":"2020-04-30","cited_by_count":81},{"id":"2002.06622","title":"Robustness Verification for Transformers","created":"2020-02-16","cited_by_count":76},{"id":"2106.00245","title":"Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models","created":"2021-06-01","cited_by_count":75},{"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":"2106.09898","title":"Bad Characters: Imperceptible NLP Attacks","created":"2021-06-18","cited_by_count":71},{"id":"1901.09981","title":"Improving Adversarial Robustness of Ensembles with Diversity Training","created":"2019-01-28","cited_by_count":69},{"id":"2107.13541","title":"Towards Robustness Against Natural Language Word Substitutions","created":"2021-07-28","cited_by_count":69},{"id":"1805.02917","title":"Interpretable Adversarial Perturbation in Input Embedding Space for Text","created":"2018-05-08","cited_by_count":67},{"id":"2006.11834","title":"AdvAug: Robust Adversarial Augmentation for Neural Machine Translation","created":"2020-06-21","cited_by_count":67},{"id":"1911.09272","title":"Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation","created":"2019-11-21","cited_by_count":60},{"id":"2009.11321","title":"Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining","created":"2020-09-23","cited_by_count":59},{"id":"2103.01498","title":"A Survey On Universal Adversarial Attack","created":"2021-03-02","cited_by_count":59},{"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":"2105.03743","title":"Certified Robustness to Text Adversarial Attacks by Randomized [MASK]","created":"2021-05-08","cited_by_count":57},{"id":"2108.12777","title":"Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution","created":"2021-08-29","cited_by_count":56},{"id":"1703.03906","title":"Massive Exploration of Neural Machine Translation Architectures","created":"2017-03-11","cited_by_count":55},{"id":"1707.02812","title":"Towards Crafting Text Adversarial Samples","created":"2017-07-10","cited_by_count":55},{"id":"1808.09408","title":"Privacy-preserving Neural Representations of Text","created":"2018-08-28","cited_by_count":54},{"id":"2103.10013","title":"Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!","created":"2021-03-18","cited_by_count":53},{"id":"2104.15135","title":"Explanation-Based Human Debugging of NLP Models: A Survey","created":"2021-04-30","cited_by_count":53},{"id":"2002.07526","title":"A Survey of Deep Learning Techniques for Neural Machine Translation","created":"2020-02-18","cited_by_count":50},{"id":"2003.01200","title":"Natural Language Processing Advancements By Deep Learning: A Survey","created":"2020-03-02","cited_by_count":50},{"id":"1906.03805","title":"Improving Neural Language Modeling via Adversarial Training","created":"2019-06-10","cited_by_count":49},{"id":"2007.06174","title":"Generating Fluent Adversarial Examples for Natural Languages","created":"2020-07-13","cited_by_count":49},{"id":"1808.08609","title":"Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge","created":"2018-08-26","cited_by_count":46},{"id":"1804.06473","title":"Robust Machine Comprehension Models via Adversarial Training","created":"2018-04-17","cited_by_count":44},{"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":"1905.07387","title":"POPQORN: Quantifying Robustness of Recurrent Neural Networks","created":"2019-05-17","cited_by_count":37},{"id":"1912.01667","title":"A Survey of Black-Box Adversarial Attacks on Computer Vision Models","created":"2019-12-03","cited_by_count":37},{"id":"2010.12729","title":"ANLIzing the Adversarial Natural Language Inference Dataset","created":"2020-10-24","cited_by_count":36},{"id":"1911.03274","title":"Imperceptible Adversarial Attacks on Tabular Data","created":"2019-11-08","cited_by_count":35},{"id":"2011.07251","title":"DebateSum: A large-scale argument mining and summarization dataset","created":"2020-11-14","cited_by_count":34},{"id":"2106.01810","title":"Defending Against Backdoor Attacks in Natural Language Generation","created":"2021-06-03","cited_by_count":33},{"id":"2203.01677","title":"Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation","created":"2022-03-03","cited_by_count":32},{"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":"2006.11627","title":"Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble","created":"2020-06-20","cited_by_count":28},{"id":"2103.11576","title":"Grey-box Adversarial Attack And Defence For Sentiment Classification","created":"2021-03-22","cited_by_count":28},{"id":"1711.04903","title":"Robust Multilingual Part-of-Speech Tagging via Adversarial Training","created":"2017-11-14","cited_by_count":21},{"id":"2005.13300","title":"Scalable Polyhedral Verification of Recurrent Neural Networks","created":"2020-05-27","cited_by_count":20},{"id":"2010.01610","title":"Adversarial Attack and Defense of Structured Prediction Models","created":"2020-10-04","cited_by_count":20},{"id":"2106.10826","title":"Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification","created":"2021-06-21","cited_by_count":20},{"id":"1608.02214","title":"Robsut Wrod Reocginiton via semi-Character Recurrent Neural Network","created":"2016-08-07","cited_by_count":18},{"id":"2102.07818","title":"Certified Robustness to Programmable Transformations in LSTMs","created":"2021-02-15","cited_by_count":18},{"id":"2106.01452","title":"BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks","created":"2021-06-02","cited_by_count":17},{"id":"2112.07605","title":"The King is Naked: on the Notion of Robustness for Natural Language Processing","created":"2021-12-13","cited_by_count":16},{"id":"1805.04680","title":"AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples","created":"2018-05-12","cited_by_count":15}],"next_cursor":"100"}