AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval

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Stephen Robertson, S. Walker, S. Jones, M. M. Hancock-Beaulieu, and M. Gatford · 1995

Earlier work this paper cites.

A deep relevance matching model for ad-hoc retrieval

Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft · 2016

Earlier work this paper cites.

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Earlier work this paper cites.

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Bhaskar Mitra and Nick Craswell · 2017

Earlier work this paper cites.

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Earlier work this paper cites.

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Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020

Earlier work this paper cites.

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Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela · 2020

Earlier work this paper cites.

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Earlier work this paper cites.

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Earlier work this paper cites.

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