General-Purpose Question-Answering with Macaw

The winograd schema challenge

H. Levesque, E. Davis, and L. Morgenstern · 2011

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Combining retrieval, statistics, and inference to answer elementary science questions

P. Clark, O. Etzioni, T. Khot, A. Sabharwal, O. Tafjord, P. D. Turney, and D. Khashabi · 2016

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How to write science questions that are easy for people and hard for computers

E. Davis · 2016

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Tracking the world state with recurrent entity networks

M. Henaff, J. Weston, A. D. Szlam, A. Bordes, and Y. LeCun · 2016

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Towards AI-Complete question answering: A set of prerequisite toy tasks

J. Weston, A. Bordes, S. Chopra, and T. Mikolov · 2016

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RACE: Large-scale reading comprehension dataset from examinations

G. Lai, Q. Xie, H. Liu, Y. Yang, and E. Hovy · 2017

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Think you have solved question answering? Try ARC, the AI2 Reasoning Challenge

Original

P. Clark, I. Cowhey, O. Etzioni, T. Khot, A. Sabharwal, C. Schoenick, and O. Tafjord · 2018

Earlier work this paper cites.

Tracking state changes in procedural text: a challenge dataset and models for process paragraph comprehension

B. Dalvi, L. Huang, N. Tandon, W. tau Yih, and P. Clark · 2018

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Worldtree: A corpus of explanation graphs for elementary science questions supporting multi-hop inference

Original

P. A. Jansen, E. Wainwright, S. Marmorstein, and C. T. Morrison · 2018

Cited alongside, same era.

Improving language understanding by generative pre-training

A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever · 2018

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Reasoning about actions and state changes by injecting commonsense knowledge

N. Tandon, B. Dalvi, J. Grus, W. tau Yih, A. Bosselut, and P. Clark · 2018

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Natural questions: a benchmark for question answering research

T. Kwiatkowski, J. Palomaki, O. Redfield, M. Collins, A. Parikh, C. Alberti, D. Epstein, I. Polosukhin, M. Kelcey, J. Devlin, K. Lee, K. N. Toutanova, L. Jones, M.-W. Chang, A. Dai, J. Uszkoreit, Q. Le, and S. Petrov · 2019

Cited alongside, same era.

Language models are few-shot learners

T. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei · 2020

Unqovering stereotyping biases via underspecified questions

T. Li, D. Khashabi, T. Khot, A. Sabharwal, and V. Srikumar · 2020

Later among the works it cites.

Exploring the limits of transfer learning with a unified text-to-text transformer

C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2020

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How much knowledge can you pack into the parameters of a language model?

A. Roberts, C. Raffel, and N. M. Shazeer · 2020

Later among the works it cites.

Think you have solved direct-answer question answering? try arc-da, the direct-answer ai2 reasoning challenge

Original

S. Bhakthavatsalam, D. Khashabi, T. Khot, B. D. Mishra, K. Richardson, A. Sabharwal, C. Schoenick, O. Tafjord, and P. Clark · 2021

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How much coffee was consumed during emnlp 2019? fermi problems: A new reasoning challenge for ai

A. Kalyan, A. Kumar, A. Chandrasekaran, A. Sabharwal, and P. Clark · 2021

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