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Solving crossword puzzles requires diverse reasoning capabilities, access to a vast amount of knowledge about language and the world, and the ability to satisfy the constraints imposed by the structure of the puzzle.
Learning and evaluating general linguistic intelligence
Dani Yogatama, Cyprien de Masson d’Autume, Jerome Connor, Tomas Kocisky, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, et al. 2019 · 1901
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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. 2019 · 1910
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What does BERT learn from multiple-choice reading comprehension datasets?
Chenglei Si, Shuohang Wang, Min-Yen Kan, and Jing Jiang. 2019 · 1910
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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Computational complexity
Christos H. Papadimitriou. 1994 · 1994
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Proverb: The probabilistic cruciverbalist
Greg A. Keim, Noam M. Shazeer, Michael L. Littman, Sushant Agarwal, Catherine M. Cheves, Joseph Fitzgerald, Jason Grosland, Fan Jiang, Shannon Pollard, and Karl Weinmeister. 1999 · 1999
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A probabilistic approach to solving crossword puzzles
Michael L. Littman, Greg A. Keim, and Noam Shazeer. 2002 · 2002
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Webcrow: A web-based system for crossword solving
Marco Ernandes, Giovanni Angelini, and Marco Gori. 2005 · 2005
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Sudoku as a constraint problem
Helmut Simonis. 2005 · 2005
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Z3: An efficient smt solver
Leonardo de Moura and Nikolaj Bjørner. 2008 · 2008
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Dr. fill: Crosswords and an implemented solver for singly weighted csps
Matthew L Ginsberg. 2011 · 2011
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Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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Enumerating infeasibility: Finding multiple muses quickly
Mark H Liffiton and Ammar Malik. 2013 · 2013
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Partial mus enumeration
Alessandro Previti and Joao Marques-Silva. 2013 · 2013
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Learning to rank answer candidates for automatic resolution of crossword puzzles
Gianni Barlacchi, Massimo Nicosia, and Alessandro Moschitti. 2014 · 2014
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Large-scale simple question answering with memory networks
Antoine Bordes, Nicolas Usunier, Sumit Chopra, and Jason Weston. 2015 · 2015
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Distributional neural networks for automatic resolution of crossword puzzles
Aliaksei Severyn, Massimo Nicosia, Gianni Barlacchi, and Alessandro Moschitti. 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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Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 2019
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Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 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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Universal adversarial triggers for attacking and analyzing nlp
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019 · 2019
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HellaSwag: Can a Machine Really Finish Your Sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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Is bert really robust? a strong baseline for natural language attack on text classification and entailment
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Rasmus Berg Palm, Ulrich Paquet, and Ole Winther. 2017 · 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 · 2017
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Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
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Wikiqa: A challenge dataset for open-domain question answering
Yi Yang, Wen-tau Yih, and Christopher Meek. 2015 · 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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Record: Bridging the gap between human and machine commonsense reading comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme. 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. 2019 · 2019
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Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020 · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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 · 2020
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CharBERT: Character-aware pre-trained language model
Wentao Ma, Yiming Cui, Chenglei Si, Ting Liu, Shijin Wang, and Guoping Hu. 2020 · 2020
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Assessing the benchmarking capacity of machine reading comprehension datasets
Saku Sugawara, Pontus Stenetorp, Kentaro Inui, and Akiko Aizawa. 2020 · 2020
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QA dataset explosion: A taxonomy of NLP resources for question answering and reading comprehension
Anna Rogers, Matt Gardner, and Isabelle Augenstein. 2021 · 2021
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Retrieval augmentation reduces hallucination in conversation
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021 · 2021
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