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Temporal reasoning is the task of predicting temporal relations of event pairs.
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 · 1901
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The timebank corpus
James Pustejovsky, Patrick Hanks, Roser Sauri, Andrew See, Robert Gaizauskas, Andrea Setzer, Dragomir Radev, Beth Sundheim, David Day, Lisa Ferro, et al. 2003 · 2003
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Veronica Latcinnik and Jonathan Berant. 2020 · 2004
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Three approaches to learning tlinks in timeml
Inderjeet Mani, Ben Wellner, Marc Verhagen, and James Pustejovsky. 2007 · 2007
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SemEval-2007 task 15: TempEval temporal relation identification
Marc Verhagen, Robert Gaizauskas, Frank Schilder, Mark Hepple, Graham Katz, and James Pustejovsky. 2007 · 2007
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SemEval-2010 task 13: TempEval-2
Marc Verhagen, Roser Saurí, Tommaso Caselli, and James Pustejovsky. 2010 · 2010
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SemEval-2013 task 1: TempEval-3: Evaluating time expressions, events, and temporal relations
Naushad UzZaman, Hector Llorens, Leon Derczynski, James Allen, Marc Verhagen, and James Pustejovsky. 2013 · 2013
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An annotation framework for dense event ordering
Taylor Cassidy, Bill McDowell, Nathanael Chambers, and Steven Bethard. 2014 · 2014
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Dense event ordering with a multi-pass architecture
Nathanael Chambers, Taylor Cassidy, Bill McDowell, and Steven Bethard. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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A corpus and cloze evaluation for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016 · 2016
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A structured learning approach to temporal relation extraction
Qiang Ning, Zhili Feng, and Dan Roth. 2017 · 2017
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e-snli: Natural language inference with natural language explanations
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Joint event and temporal relation extraction with shared representations and structured prediction
Rujun Han, Qiang Ning, and Nanyun Peng. 2019 · 2019
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Nils Reimers and Iryna Gurevych. 2019 · 2019
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Attention is not not explanation
Discourse-level event temporal ordering with uncertainty-guided graph completion
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TIMERS: Document-level temporal relation extraction
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Teach me to explain: A review of datasets for explainable nlp
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Temporal reasoning on implicit events from distant supervision
Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, and Dan Roth. 2021 · 2021
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Few-shot self-rationalization with natural language prompts
Ana Marasović, Iz Beltagy, Doug Downey, and Matthew E. Peters. 2022 · 2022
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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ERASER: A benchmark to evaluate rationalized NLP models
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Transformers: State-of-the-art natural language processing
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Temporal common sense acquisition with minimal supervision
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Explanations for CommonsenseQA: New Dataset and Models
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Gray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Selecting optimal context sentences for event-event relation extraction
Hieu Man Duc Trong, Nghia Ngo Trung, Linh Van Ngo, and Thien Huu Nguyen. 2022 · 2022
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