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Event schemas are a form of world knowledge about the typical progression of events.
Scripts, plans, and knowledge
Roger C Schank and Robert P Abelson. 1975 · 1975
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Maintaining knowledge about temporal intervals
James F. Allen. 1983 · 1983
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String comparator metrics and enhanced decision rules in the fellegi-sunter model of record linkage
William E Winkler. 1990 · 1990
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A fast and effective heuristic for the feedback arc set problem
Peter Eades, Xuemin Lin, and William F Smyth. 1993 · 1993
Earlier work this paper cites.
Unsupervised learning of narrative event chains
Nathanael Chambers and Dan Jurafsky. 2008 · 2008
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Refining event extraction through unsupervised cross-document inference
Heng Ji and Ralph Grishman. 2008 · 2008
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Unsupervised learning of narrative schemas and their participants
Nathanael Chambers and Dan Jurafsky. 2009 · 2009
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Skip n-grams and ranking functions for predicting script events
Bram Jans, Steven Bethard, Ivan Vulić, and Marie Francine Moens. 2012 · 2012
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HiEve: A corpus for extracting event hierarchies from news stories
Goran Glavaš, Jan Šnajder, Marie-Francine Moens, and Parisa Kordjamshidi. 2014 · 2014
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Statistical script learning with multi-argument events
Karl Pichotta and Raymond Mooney. 2014 · 2014
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Script induction as language modeling
Rachel Rudinger, Pushpendre Rastogi, Francis Ferraro, and Benjamin Van Durme. 2015a · 2015
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Script induction as language modeling
Rachel Rudinger, Pushpendre Rastogi, Francis Ferraro, and Benjamin Van Durme. 2015b · 2015
Earlier work this paper cites.
Improving event prediction by representing script participants
Simon Ahrendt and Vera Demberg. 2016 · 2016
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Ms marco: A human generated machine reading comprehension dataset
Daniel Fernando Campos, Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, Li Deng, and Bhaskar Mitra. 2016 · 2016
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What happens next? event prediction using a compositional neural network model
Mark Granroth-Wilding and Stephen Clark. 2016 · 2016
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Statistical script learning with recurrent neural networks
Karl Pichotta and Raymond Mooney. 2016 · 2016
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Integrating order information and event relation for script event prediction
Zhongqing Wang, Yue Zhang, and Ching-Yun Chang. 2017 · 2017
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Constructing narrative event evolutionary graph for script event prediction
Zhongyang Li, Xiao Ding, and Ting Liu. 2018 · 2018
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Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations
Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021a · 2021
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In-batch negatives for knowledge distillation with tightly-coupled teachers for dense retrieval
Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. 2021b · 2021
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Scaling language models: Methods, analysis & insights from training gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John F. J. Mellor, Irina Higgins, Antonia Creswell, Nathan McAleese, Amy Wu, Erich Elsen, Siddhant M. Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, L. Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, N. K. Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Tobias Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew G. Johnson, Blake A. Hechtman, Laura Weidinger, Iason Gabriel, William S. Isaac, Edward Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem W. Ayoub, Jeff Stanway, L. L. Bennett, Demis Hassabis, Koray Kavukcuoglu, and Geoffrey Irving. 2021 · 2021
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Event representations with tensor-based compositions
Noah Weber, Niranjan Balasubramanian, and Nathanael Chambers. 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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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, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff 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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Colbert: Efficient and effective passage search via contextualized late interaction over bert
O. Khattab and Matei A. Zaharia. 2020 · 2020
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Connecting the dots: Event graph schema induction with path language modeling
Manling Li, Qi Zeng, Ying Lin, Kyunghyun Cho, Heng Ji, Jonathan May, Nathanael Chambers, and Clare Voss. 2020 · 2020
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Reasoning about goals, steps, and temporal ordering with WikiHow
Li Zhang, Qing Lyu, and Chris Callison-Burch. 2020 · 2020
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Measuring and improving consistency in pretrained language models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg. 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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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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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek B Rao, Parker Barnes, Yi Tay, Noam M. Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Benton C. Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier García, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Oliveira Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Díaz, Orhan Firat, Michele Catasta, Jason Wei, Kathleen S. Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022 · 2022
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Zero-shot on-the-fly event schema induction
Rotem Dror, Haoyu Wang, and Dan Roth. 2022 · 2022
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RESIN-11: Schema-guided event prediction for 11 newsworthy scenarios
Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, and Heng Ji. 2022 · 2022
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Can language models learn from explanations in context?
Andrew Kyle Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, and Felix Hill. 2022 · 2022
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What do large language models learn about scripts?
Abhilasha Sancheti and Rachel Rudinger. 2022 · 2022
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Rationale-augmented ensembles in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou. 2022 · 2022
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022 · 2022
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Event schema induction with double graph autoencoders
Xiaomeng Jin, Manling Li, and Heng Ji. 2022 · 2025
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