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We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment.
A bert baseline for the natural questions
Chris Alberti, Kenton Lee, and Michael Collins. 2019b · 1901
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Did the model understand the question?
Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, and Kedar Dhamdhere. 2018 · 1906
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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 · 1907
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Interactive machine comprehension with information seeking agents
Xingdi Yuan, Jie Fu, Marc-Alexandre Côté, Yi Tay, Christopher Pal, and Adam Trischler. 2019 · 1908
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Falcon: Boosting knowledge for answer engines
Sanda Harabagiu, Dan Moldovan, Marius Pasca, Rada Mihalcea, Mihai Surdeanu, Razvan Bunescu, Roxana Girju, Vasile Rus, and Paul Morarescu. 2000 · 2000
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Building a question answering test collection
Ellen M Voorhees and Dawn M Tice. 2000 · 2000
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Evaluating the evaluation: A case study using the TREC 2002 question answering track
Ellen M. Voorhees. 2003 · 2002
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Patterns between interactive intentions and information-seeking strategies
Hong (Iris) Xie. 2002 · 2002
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Extracting exact answers using a meta question answering system
Luiz Augusto Pizzato and Diego Molla. 2005 · 2005
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Exploratory search: From finding to understanding
Gary Marchionini. 2006 · 2006
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Analyzing and evaluating query reformulation strategies in web search logs
Jeff Huang and Efthimis Efthimiadis. 2009 · 2009
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Patterns of query reformulation during web searching
B. J. Jansen, D. L. Booth, and A. Spink. 2009 · 2009
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Building Watson: An Overview of the DeepQA Project
David Ferrucci, Eric Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya A. Kalyanpur, Adam Lally, J. William Murdock, Eric Nyberg, John Prager, Nico Schlaefer, and Chris Welty. 2010 · 2010
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How do children reformulate their search queries?
Sophie Rutter, Nigel Ford, and Paul Clough. 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
Cited alongside, same era.
Task-oriented query reformulation with reinforcement learning
Rodrigo Nogueira and Kyunghyun Cho. 2017 · 2017
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NewsQA: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
Cited alongside, same era.
Ask the right questions: Active question reformulation with reinforcement learning
Multi-step retriever-reader interaction for scalable open-domain question answering
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, and Andrew McCallum. 2019 · 2019
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Episodic memory reader: Learning what to remember for question answering from streaming data
Moonsu Han, Minki Kang, Hyunwoo Jung, and Sung Ju Hwang. 2019 · 2019
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Neural speed reading with structural-jump-LSTM
Christian Hansen, Casper Hansen, Stephen Alstrup, Jakob Grue Simonsen, and Christina Lioma. 2019 · 2019
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Optimizing agent behavior over long time scales by transporting value
Chia-Chun Hung, Timothy Lillicrap, Josh Abramson, Yan Wu, Mehdi Mirza, Federico Carnevale, Arun Ahuja, and Greg Wayne. 2019 · 2019
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Andrea Gesmundo, Neil Houlsby, Wojciech Gajewski, and Wei Wang. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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IMPALA: Scalable distributed deep-RL with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu. 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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Neural speed reading via skim-RNN
Minjoon Seo, Sewon Min, Ali Farhadi, and Hannaneh Hajishirzi. 2018 · 2018
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The fact extraction and VERification (FEVER) shared task
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 2019
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Answering while summarizing: Multi-task learning for multi-hop QA with evidence extraction
Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata, Atsushi Otsuka, Itsumi Saito, Hisako Asano, and Junji Tomita. 2019 · 2019
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Probing neural network comprehension of natural language arguments
Timothy Niven and Hung-Yu Kao. 2019 · 2019
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The Joy of Search: A Google Insider’s Guide to Going Beyond the Basics
Daniel M. Russell. 2019 · 2019
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Trick me if you can: Human-in-the-loop generation of adversarial question answering examples
Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. 2019 · 2019
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
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