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Script knowledge is critical for humans to understand the broad daily tasks and routine activities in the world.
Assessing bert’s syntactic abilities
Yoav Goldberg. 2019 · 1901
Earlier work this paper cites.
Comet: Commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019 · 1906
Earlier work this paper cites.
What does bert look at? an analysis of bert’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D Manning. 2019 · 1906
Earlier work this paper cites.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 1906
Earlier work this paper cites.
Inducing syntactic trees from bert representations
Rudolf Rosa and David Mareček. 2019 · 1906
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Event representation learning enhanced with external commonsense knowledge
Xiao Ding, Kuo Liao, Ting Liu, Zhongyang Li, and Junwen Duan. 2019 · 1909
Earlier work this paper cites.
Cosmos qa: Machine reading comprehension with contextual commonsense reasoning
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 1909
Earlier work this paper cites.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2019a · 1909
Earlier work this paper cites.
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
Earlier work this paper cites.
Do attention heads in bert track syntactic dependencies?
Phu Mon Htut, Jason Phang, Shikha Bordia, and Samuel R Bowman. 2019 · 1911
Earlier work this paper cites.
Scripts, plans, and knowledge
Roger C Schank and Robert P Abelson. 1975 · 1975
Earlier work this paper cites.
The handbook of artificial intelligence
Edward A Feigenbaum, Avron Barr, and Paul R Cohen. 1981 · 1981
Earlier work this paper cites.
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
Earlier work this paper cites.
Common sense data acquisition for indoor mobile robots
Rakesh Gupta and Mykel J. Kochenderfer. 2004 · 2004
Earlier work this paper cites.
Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren. 2020 · 2005
Earlier work this paper cites.
How context affects language models’ factual predictions
Fabio Petroni, Patrick Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2020 · 2005
Earlier work this paper cites.
Unsupervised learning of narrative event chains
Nathanael Chambers and Daniel Jurafsky. 2008 · 2008
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Language models are open knowledge graphs
Chenguang Wang, Xiao Liu, and Dawn Song. 2020 · 2010
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Skip n-grams and ranking functions for predicting script events
Bram Jans, Steven Bethard, Ivan Vulic, and Marie-Francine Moens. 2012 · 2012
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Event schema induction with a probabilistic entity-driven model
Nathanael Chambers. 2013 · 2013
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Story generation with crowdsourced plot graphs
Boyang Li, Stephen Lee-Urban, George Johnston, and Mark Riedl. 2013 · 2013
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Scripts, plans, goals, and understanding: An inquiry into human knowledge structures
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Story ending generation with incremental encoding and commonsense knowledge
Jian Guan, Yansen Wang, and Minlie Huang. 2019 · 2019
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Designing and interpreting probes with control tasks
John Hewitt and Percy Liang. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019b · 2019
Later among the works it cites.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick S. H. Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander H. Miller. 2019c · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Roger C Schank and Robert P Abelson. 2013 · 2013
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Statistical script learning with multi-argument events
Karl Pichotta and Raymond J. Mooney. 2014 · 2014
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A unified bayesian model of scripts, frames and language
Francis Ferraro and Benjamin Van Durme. 2016 · 2016
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Temporal anchoring of events for the timebank corpus
Nils Reimers, Nazanin Dehghani, and Iryna Gurevych. 2016 · 2016
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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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What do neural machine translation models learn about morphology?
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass. 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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What do you learn from context? probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R. Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
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A hybrid model for globally coherent story generation
Fangzhou Zhai, Vera Demberg, Pavel Shkadzko, Wei Shi, and Asad Sayeed. 2019 · 2019
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Inducing relational knowledge from bert
Zied Bouraoui, Jose Camacho-Collados, and Steven Schockaert. 2020 · 2020
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Scene restoring for narrative machine reading comprehension
Zhixing Tian, Yuanzhe Zhang, Kang Liu, Jun Zhao, Yantao Jia, and Zhicheng Sheng. 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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Evaluating commonsense in pre-trained language models
Xuhui Zhou, Yue Zhang, Leyang Cui, and Dandan Huang. 2020 · 2020
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Identify, align, and integrate: Matching knowledge graphs to commonsense reasoning tasks
Lisa Bauer and Mohit Bansal. 2021 · 2021
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021 · 2021
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Probing for bridging inference in transformer language models
Onkar Pandit and Yufang Hou. 2021 · 2021
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Inferring commonsense explanations as prompts for future event generation
Li Lin, Yixin Cao, Lifu Huang, Shuang Li, Xuming Hu, Lijie Wen, and Jianmin Wang. 2022 · 2022
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