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Deep neural network based question answering (QA) models are neither robust nor explainable in many cases.
Roberta: A robustly optimized bert pretraining approach
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Megatron-lm: Training multi-billion parameter language models using gpu model parallelism
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HuggingFace’s Transformers: State-of-the-art Natural Language Processing
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Two Causal Principles for Improving Visual Dialog
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Robust natural language inference models with example forgetting
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Divergence measures based on the Shannon entropy
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Counterfactual thinking
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Direct and indirect effects
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Adversarial Filters of Dataset Biases
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Unbiased scene graph generation from biased training
Tang, K.; Niu, Y.; Huang, J.; Shi, J.; and Zhang, H. 2020 · 2002
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Counterfactual Samples Synthesizing for Robust Visual Question Answering
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Causality: models, reasoning, and inference
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Deconfounded image captioning: A causal retrospect
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Social mechanisms and causal inference
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Causation and causal inference in epidemiology
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Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance
Utama, P. A.; Moosavi, N. S.; and Gurevych, I. 2020 · 2005
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Counterfactual VQA: A Cause-Effect Look at Language Bias
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Causal inference in statistics: An overview
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Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
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Interventional Few-Shot Learning
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Causal Intervention for Weakly-Supervised Semantic Segmentation
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Targeted learning: causal inference for observational and experimental data
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Mctest: A challenge dataset for the open-domain machine comprehension of text
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Intriguing properties of neural networks
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A large annotated corpus for learning natural language inference
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Counterfactuals and causal inference
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Counterfactual Visual Explanations
Goyal, Y.; Wu, Z.; Ernst, J.; Batra, D.; Parikh, D.; and Lee, S. 2019 · 2019
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Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects
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Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual
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Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
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Rajpurkar, P.; Zhang, J.; Lopyrev, K.; and Liang, P. 2016 · 2016
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RACE: Large-scale ReAding Comprehension Dataset From Examinations
Lai, G.; Xie, Q.; Liu, H.; Yang, Y.; and Hovy, E. 2017 · 2017
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Generating Natural Language Adversarial Examples
Alzantot, M.; Sharma, Y.; Elgohary, A.; Ho, B.-J.; Srivastava, M.; and Chang, K.-W. 2018 · 2018
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On Adversarial Examples for Character-Level Neural Machine Translation
Ebrahimi, J.; Lowd, D.; and Dou, D. 2018 · 2018
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HotFlip: White-Box Adversarial Examples for Text Classification
Ebrahimi, J.; Rao, A.; Lowd, D.; and Dou, D. 2018 · 2018
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Pathologies of Neural Models Make Interpretations Difficult
Feng, S.; Wallace, E.; Grissom II, A.; Iyyer, M.; Rodriguez, P.; and Boyd-Graber, J. 2018 · 2018
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Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Iyyer, M.; Wieting, J.; Gimpel, K.; and Zettlemoyer, L. 2018 · 2018
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Huang, P.-S.; Stanforth, R.; Welbl, J.; Dyer, C.; Yogatama, D.; Gowal, S.; Dvijotham, K.; and Kohli, P. 2019 · 2019
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Certified Robustness to Adversarial Word Substitutions
Jia, R.; Raghunathan, A.; Göksel, K.; and Liang, P. 2019 · 2019
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Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
McCoy, T.; Pavlick, E.; and Linzen, T. 2019 · 2019
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Generating natural language adversarial examples through probability weighted word saliency
Ren, S.; Deng, Y.; He, K.; and Che, W. 2019 · 2019
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DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension
Sun, K.; Yu, D.; Chen, J.; Yu, D.; Choi, Y.; and Cardie, C. 2019 · 2019
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Universal Adversarial Triggers for Attacking and Analyzing NLP
Wallace, E.; Feng, S.; Kandpal, N.; Gardner, M.; and Singh, S. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z.; Dai, Z.; Yang, Y.; Carbonell, J.; Salakhutdinov, R. R.; and Le, Q. V. 2019 · 2019
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QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization
Yeh, Y.-T.; and Chen, Y.-N. 2019 · 2019
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Generating Fluent Adversarial Examples for Natural Languages
Zhang, H.; Zhou, H.; Miao, N.; and Li, L. 2019 · 2019
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PAWS: Paraphrase Adversaries from Word Scrambling
Zhang, Y.; Baldridge, J.; and He, L. 2019 · 2019
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Counterfactual vision and language learning
Abbasnejad, E.; Teney, D.; Parvaneh, A.; Shi, J.; and Hengel, A. v. d. 2020 · 2020
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Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
Kaushik, D.; Hovy, E.; and Lipton, Z. C. 2020 · 2020
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A Robust Adversarial Training Approach to Machine Reading Comprehension
Liu, K.; Liu, X.; Yang, A.; Liu, J.; Su, J.; Li, S.; and She, Q. 2020 · 2020
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Visual commonsense r-cnn
Wang, T.; Huang, J.; Zhang, H.; and Sun, Q. 2020 · 2020
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Adversarial Examples for Evaluating Reading Comprehension Systems
Jia, R.; and Liang, P. 2017 · 2031
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