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Machine Reading Comprehension (MRC) is an important testbed for evaluating models' natural language understanding (NLU) ability.
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GloVe: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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SQuAD: 100, 000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Adversarial Examples for Evaluating Reading Comprehension Systems
Robin Jia and Percy Liang. 2017 · 2017
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RACE: Large-scale ReAding Comprehension Dataset From Examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard H. Hovy. 2017 · 2017
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Generating Natural Language Adversarial Examples
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Synthetic and Natural Noise Both Break Neural Machine Translation
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HotFlip: White-Box Adversarial Examples for NLP
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Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
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How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks
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Semantically Equivalent Adversarial Rules for Debugging NLP models
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Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering
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Robust Machine Comprehension Models via Adversarial Training
Yicheng Wang and Mohit Bansal. 2018 · 2018
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A Span-Extraction Dataset for Chinese Machine Reading Comprehension
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension
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Universal adversarial triggers for attacking and analyzing NLP
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XLNet: Generalized Autoregressive Pretraining for Language Understanding
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Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020 · 2020
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
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Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
Cited alongside, same era.
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension
Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen. 2019 · 2019
Cited alongside, same era.
Improving the Robustness of Question Answering Systems to Question Paraphrasing
Wee Chung Gan and Hwee Tou Ng. 2019 · 2019
Cited alongside, same era.
Generating Distractors for Reading Comprehension Questions from Real Examinations
Yifan Gao, Lidong Bing, Piji Li, Irwin King, and Michael R. Lyu. 2019 · 2019
Cited alongside, same era.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
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Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency
Shuhuai Ren, Yihe Deng, Kun He, and Wanxiang Che. 2019 · 2019
Cited alongside, same era.
What does BERT learn from multiple-choice reading comprehension datasets?
Chenglei Si, Shuohang Wang, Min-Yen Kan, and Jing Jiang. 2019 · 2019
Cited alongside, same era.
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension
Dheeru Dua, Ananth Gottumukkala, Alon Talmor, Sameer Singh, and Matt Gardner. 2019a
Cited in the paper.
Zhen-Zhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
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Adversarial NLI: A New Benchmark for Natural Language Understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
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Beyond Accuracy: Behavioral Testing of NLP models with CheckList
Marco Túlio Ribeiro, Tongshuang Wu, C. Guestrin, and Sameer Singh. 2020 · 2020
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Hongxuan Tang, Jing Liu, Hongyu Li, Yu Hong, Hua Wu, and Hai-Feng Wang. 2020 · 2020
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Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks
Winston Wu, Dustin Arendt, and Svitlana Volkova. 2020 · 2020
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Textual Adversarial Attack as Combinatorial Optimization
Yuan Zang, Chenghao Yang, Fanchao Qi, Zhiyuan Liu, Meng Zhang, Qun Liu, and Maosong Sun. 2020 · 2020
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Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension
Xiaorui Zhou, Senlin Luo, and Yunfang Wu. 2020 · 2020
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