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Cryptic crosswords, the dominant crossword variety in the UK, are a promising target for advancing NLP systems that seek to process semantically complex, highly compositional language.
Cryptic crossword clue interpreter
M Hart and Robert H Davis · 1992
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C. Fellbaum · 1998
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Catastrophic forgetting in connectionist networks
Robert M. French · 1999
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Solving crossword puzzles as probabilistic constraint satisfaction
Noam M Shazeer, Michael L Littman, and Greg A Keim · 1999
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Using the bnc to produce dialectic cryptic crossword clues
D Hardcastle · 2001
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A probabilistic approach to solving crossword puzzles
Michael L Littman, Greg A Keim, and Noam Shazeer · 2002
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Riddle posed by computer (6): the computer generation of cryptic crossword clues
David Hardcastle · 2007
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Dr. fill: Crosswords and an implemented solver for singly weighted csps
Matthew L Ginsberg · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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The funny thing about incongruity: A computational model of humor in puns
Justine T Kao, Roger Levy, and Noah D Goodman · 2013
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rdeits/cryptics, 2015
Robin Deits · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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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
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The penny drops: Investigating insight through the medium of cryptic crosswords
Kathryn J Friedlander and Philip A Fine · 2018
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Taku Kudo and John Richardson · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern · 2018
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Language models are few-shot learners
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
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Fluid intelligence is key to successful cryptic crossword solving
Kathryn J Friedlander and Philip A Fine · 2020
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Injecting numerical reasoning skills into language models
Mor Geva, Ankit Gupta, and Jonathan Berant · 2020
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Emergent linguistic structure in artificial neural networks trained by self-supervision
Christopher D Manning, Kevin Clark, John Hewitt, Urvashi Khandelwal, and Omer Levy · 2020
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The next decade in AI: Four steps towards robust artificial intelligence
Gary Marcus · 2020
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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He He, Nanyun Peng, and Percy Liang · 2019
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bjascob/lemminflect, 2019
Brad Jascob · 2019
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Pun-gan: Generative adversarial network for pun generation
Fuli Luo, Shunyao Li, Pengcheng Yang, Baobao Chang, Zhifang Sui, Xu Sun, et al · 2019
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Language models are unsupervised multitask learners
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QuaRTz: An open-domain dataset of qualitative relationship questions
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Exploring the limits of transfer learning with a unified text-to-text transformer
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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, Rémi 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 M. Rush · 2020
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Muppet: Massive multi-task representations with pre-finetuning
Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, Xilun Chen, Luke Zettlemoyer, and Sonal Gupta · 2021
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Cryptonite: A cryptic crossword benchmark for extreme ambiguity in language, 2021
Avia Efrat, Uri Shaham, Dan Kilman, and Omer Levy · 2021
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Models in a spelling bee: Language models implicitly learn the character composition of tokens
Itay Itzhak and Omer Levy · 2021
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URL http://xd.saul.pw/data/
Saul Pwanson, 2021 · 2021
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Lime: Learning inductive bias for primitives of mathematical reasoning
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