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Entities and events are crucial to natural language reasoning and common in procedural texts.
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, et al. 2020 · 1901
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Automated knowledge acquisition for instructional text generation
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Dorin Comaniciu, Visvanathan Ramesh, and Peter Meer. 2003 · 2003
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Feature selection in categorizing procedural expressions
Mineki Takechi, Takenobu Tokunaga, Yuji Matsumoto, and Hozumi Tanaka. 2003 · 2003
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Simultaneous localization, mapping and moving object tracking
Chieh-Chih Wang, Charles Thorpe, Sebastian Thrun, Martial Hebert, and Hugh Durrant-Whyte. 2007 · 2007
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Investigating the structure of procedural texts for answering how-to questions
Estelle Delpech and Patrick Saint-Dizier. 2008 · 2008
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Automatic construction of a large-scale situation ontology by mining how-to instructions from the web
Yuchul Jung, Jihee Ryu, Kyung-min Kim, and Sung-Hyon Myaeng. 2010 · 2010
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Joint entity and event coreference resolution across documents
Heeyoung Lee, Marta Recasens, Angel Chang, Mihai Surdeanu, and Dan Jurafsky. 2012 · 2012
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Automatically extracting procedural knowledge from instructional texts using natural language processing
Ziqi Zhang, Philip Webster, Victoria Uren, Andrea Varga, and Fabio Ciravegna. 2012 · 2012
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Counterfactuals
David Lewis. 2013 · 2013
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Towards ai-complete question answering: A set of prerequisite toy tasks
Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M Rush, Bart Van Merriënboer, Armand Joulin, and Tomas Mikolov. 2015 · 2015
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Simpler context-dependent logical forms via model projections
Reginald Long, Panupong Pasupat, and Percy Liang. 2016 · 2016
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Simulating action dynamics with neural process networks
Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, and Yejin Choi. 2017 · 2017
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Distilling task knowledge from how-to communities
Cuong Xuan Chu, Niket Tandon, and Gerhard Weikum. 2017 · 2017
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Tracking state changes in procedural text: a challenge dataset and models for process paragraph comprehension
Bhavana Dalvi, Lifu Huang, Niket Tandon, Wen-tau Yih, and Peter Clark. 2018 · 2018
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The narrativeqa reading comprehension challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018 · 2018
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Jointly multiple events extraction via attention-based graph information aggregation
Xiao Liu, Zhunchen Luo, and Heyan Huang. 2018 · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
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Learning procedures from text: Codifying how-to procedures in deep neural networks
Hogun Park and Hamid Reza Motahari Nezhad. 2018 · 2018
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The Book of Why: The New Science of Cause and Effect , 1st edition
Judea Pearl and Dana Mackenzie. 2018 · 2018
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The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant. 2018 · 2018
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Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 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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Revisiting joint modeling of cross-document entity and event coreference resolution
Shany Barhom, Vered Shwartz, Alon Eirew, Michael Bugert, Nils Reimers, and Ido Dagan. 2019 · 2019
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BAG: Bi-directional attention entity graph convolutional network for multi-hop reasoning question answering
Yu Cao, Meng Fang, and Dacheng Tao. 2019 · 2019
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Everything happens for a reason: Discovering the purpose of actions in procedural text
Bhavana Dalvi, Niket Tandon, Antoine Bosselut, Wen-tau Yih, and Peter Clark. 2019 · 2019
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Question answering by reasoning across documents with graph convolutional networks
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2019 · 2019
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Cognitive graph for multi-hop reading comprehension at scale
Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, and Jie Tang. 2019 · 2019
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Be consistent! improving procedural text comprehension using label consistency
Xinya Du, Bhavana Dalvi, Niket Tandon, Antoine Bosselut, Wen-tau Yih, Peter Clark, and Claire Cardie. 2019 · 2019
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Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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Reasoning about goals, steps, and temporal ordering with WikiHow
Li Zhang, Qing Lyu, and Chris Callison-Burch. 2020c · 2020
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Li Du, Xiao Ding, Ting Liu, and Bing Qin. 2021 · 2021
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
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Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019 · 2019
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The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures
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Show your work: Scratchpads for intermediate computation with language models
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Visual goal-step inference using wikiHow
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Holistic evaluation of language models
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