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To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just "good enough" in the context of imperfect QA datasets.
A BERT baseline for the Natural Questions
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Knowing more about questions can help: Improving calibration in question answering
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REALM: Retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2002
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Factoid question answering over unstructured and structured web content
Silviu Cucerzan and Eugene Agichtein. 2005 · 2005
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The PASCAL Recognising Textual Entailment Challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Methods for using textual entailment in open-domain question answering
Sanda Harabagiu and Andrew Hickl. 2006 · 2006
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A confidence model for syntactically-motivated entailment proofs
Asher Stern and Ido Dagan. 2011 · 2011
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MCTest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher J.C. Burges, and Erin Renshaw. 2013 · 2013
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A large annotated corpus for learning natural language inference
Samuel Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
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Teaching machines to read and comprehend
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
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An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition
George Tsatsaronis, Georgios Balikas, Prodromos Malakasiotis, Ioannis Partalas, Matthias Zschunke, Michael R Alvers, Dirk Weissenborn, Anastasia Krithara, Sergios Petridis, Dimitris Polychronopoulos, Yannis Almirantis, John Pavlopoulos, Nicolas Baskiotis, Patrick Gallinari, Thierry Artieres, Axel Ngonga, Norman Heino, Eric Gaussier, Liliana Barrio-Alvers, Michael Schroeder, Ion Androutsopoulos, and Georgios Paliouras. 2015 · 2015
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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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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer. 2017 · 2017
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Towards improving abstractive summarization via entailment generation
Ramakanth Pasunuru, Han Guo, and Mohit Bansal. 2017 · 2017
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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
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Transforming question answering datasets into natural language inference datasets
Dorottya Demszky, Kelvin Guu, and Percy Liang. 2018 · 2018
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AllenNLP: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2018 · 2018
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SciTail: A textual entailment dataset from science question answering
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
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Generative question answering: Learning to answer the whole question
Mike Lewis and Angela Fan. 2018 · 2018
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Ensure the correctness of the summary: Incorporate entailment knowledge into abstractive sentence summarization
Haoran Li, Junnan Zhu, Jiajun Zhang, and Chengqing Zong. 2018 · 2018
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Know What You Don’t Know: Unanswerable Questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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FEVER: a Large-scale Dataset for Fact Extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Dialogue natural language inference
Sean Welleck, Jason Weston, Arthur Szlam, and Kyunghyun Cho. 2019 · 2019
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Qainfomax: Learning robust question answering system by mutual information maximization
Yi-Ting Yeh and Yun-Nung Chen. 2019 · 2019
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Evaluating models’ local decision boundaries via contrast sets
Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou. 2020 · 2020
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Selective question answering under domain shift
Amita Kamath, Robin Jia, and Percy Liang. 2020 · 2020
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More bang for your buck: Natural perturbation for robust question answering
Daniel Khashabi, Tushar Khot, and Ashish Sabharwal. 2020 · 2020
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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Robust machine comprehension models via adversarial training
Yicheng Wang and Mohit Bansal. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Understanding dataset design choices for multi-hop reasoning
Jifan Chen and Greg Durrett. 2019 · 2019
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 2019
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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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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo FR Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
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A robust adversarial training approach to machine reading comprehension
Kai Liu, Xin Liu, An Yang, Jing Liu, Jinsong Su, Sujian Li, and Qiaoqiao She. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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No answer is better than wrong answer: A reflection model for document level machine reading comprehension
Xuguang Wang, Linjun Shou, Ming Gong, Nan Duan, and Daxin Jiang. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Universal natural language processing with limited annotations: Try few-shot textual entailment as a start
Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev, Richard Socher, and Caiming Xiong. 2020 · 2020
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Robust reading comprehension with linguistic constraints via posterior regularization
Mantong Zhou, Minlie Huang, and Xiaoyan Zhu. 2020 · 2020
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Challenges in information-seeking QA: Unanswerable questions and paragraph retrieval
Akari Asai and Eunsol Choi. 2021 · 2021
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Robust question answering through sub-part alignment
Jifan Chen and Greg Durrett. 2021 · 2021
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Decontextualization: Making sentences stand-alone
Eunsol Choi, Jennimaria Palomaki, Matthew Lamm, Tom Kwiatkowski, Dipanjan Das, and Michael Collins. 2021 · 2021
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Which linguist invented the lightbulb? presupposition verification for question-answering
Najoung Kim, Ellie Pavlick, Burcu Karagol Ayan, and Deepak Ramachandran. 2021 · 2021
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Looking beyond sentence-level natural language inference for question answering and text summarization
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, and Kartik Talamadupula. 2021 · 2021
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
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