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We address the general task of structured commonsense reasoning: given a natural language input, the goal is to generate a graph such as an event -- or a reasoning-graph.
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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The elements of artificial intelligence: an introduction using LISP
Steven L Tanimoto. 1987 · 1987
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The birth of prolog
Alain Colmerauer and Philippe Roussel. 1996 · 1996
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An improved algorithm for matching large graphs
Luigi Pietro Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento. 2001 · 2001
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Drawing graphs with dot
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An exact graph edit distance algorithm for solving pattern recognition problems
Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, and Patrick Martineau. 2015 · 2015
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A corpus and evaluation framework for deeper understanding of commonsense stories
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A crowdsourced database of event sequence descriptions for the acquisition of high-quality script knowledge
Lilian D. A. Wanzare, Alessandra Zarcone, Stefan Thater, and Manfred Pinkal. 2016 · 2016
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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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Virtualhome: Simulating household activities via programs
Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, and Antonio Torralba. 2018 · 2018
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Program guided agent
Shao-Hua Sun, Te-Lin Wu, and Joseph J Lim. 2019 · 2019
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WIQA: A dataset for “what if…” reasoning over procedural text
Niket Tandon, Bhavana Dalvi, Keisuke Sakaguchi, Peter Clark, and Antoine Bosselut. 2019 · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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 · 2020
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Bertscore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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Learning To Retrieve Prompts for In-Context Learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2021 · 2021
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ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning
Swarnadeep Saha, Prateek Yadav, Lisa Bauer, and Mohit Bansal. 2021 · 2021
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proScript: Partially Ordered Scripts Generation
Keisuke Sakaguchi, Chandra Bhagavatula, Ronan Le Bras, Niket Tandon, Peter Clark, and Yejin Choi. 2021 · 2021
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Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021 · 2021
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Training verifiers to solve math word problems
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What Makes Good In-Context Examples for GPT-$3$?
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Could you give me a hint ? generating inference graphs for defeasible reasoning
Aman Madaan, Dheeraj Rajagopal, Niket Tandon, Yiming Yang, and Eduard Hovy. 2021a · 2021
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Think about it! improving defeasible reasoning by first modeling the question scenario
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Neural language modeling for contextualized temporal graph generation
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