Fetching the paper…
Reading the bibliography…
Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events.
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.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 1909
Earlier work this paper cites.
“cloze procedure”: A new tool for measuring readability
Wilson L Taylor. 1953 · 1953
Earlier work this paper cites.
A model for temporal references and its application in a question answering program
Bertram C Bruce. 1972 · 1972
Earlier work this paper cites.
Mechanizing temporal knowledge
Kenneth Kahn and G.Anthony Gorry. 1977 · 1977
Earlier work this paper cites.
The measurement of observer agreement for categorical data
J Richard Landis and Gary G Koch. 1977 · 1977
Earlier work this paper cites.
Maintaining knowledge about temporal intervals
James F Allen. 1983 · 1983
Earlier work this paper cites.
Towards a general theory of action and time
James F Allen. 1984 · 1984
Earlier work this paper cites.
Annotating events and temporal information in newswire texts
Andrea Setzer and Robert J Gaizauskas. 2000 · 2000
Earlier work this paper cites.
Towards a human-like open-domain chatbot
Daniel Adiwardana, Minh-Thang Luong, David R So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, et al. 2020 · 2001
Earlier work this paper cites.
Temporal and event information in natural language text
James Pustejovsky, Robert Knippen, Jessica Littman, and Roser Saurí. 2005 · 2005
Earlier work this paper cites.
Learning sentence-internal temporal relations
Mirella Lapata and Alex Lascarides. 2006 · 2006
Earlier work this paper cites.
Classifying temporal relations between events
Nathanael Chambers, Shan Wang, and Dan Jurafsky. 2007 · 2007
Earlier work this paper cites.
Learning temporal information for states and events
Z. Kozareva and E. Hovy. 2011 · 2011
Earlier work this paper cites.
Parsing time: Learning to interpret time expressions
Gabor Angeli, Christopher D Manning, and Dan Jurafsky. 2012 · 2012
Earlier work this paper cites.
Sutime: A library for recognizing and normalizing time expressions
Angel X Chang and Christopher D Manning. 2012 · 2012
Cited alongside, same era.
Joint inference for event timeline construction
Quang Do, Wei Lu, and Dan Roth. 2012 · 2012
Cited alongside, same era.
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
Cited alongside, same era.
Context-dependent semantic parsing for time expressions
Kenton Lee, Yoav Artzi, Jesse Dodge, and Luke Zettlemoyer. 2014 · 2014
Cited alongside, same era.
Event cognition
Gabriel A Radvansky and Jeffrey M Zacks. 2014 · 2014
Cited alongside, same era.
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
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.
“going on a vacation” takes longer than “going for a walk”: A study of temporal commonsense understanding
Ben Zhou, Daniel Khashabi, Qiang Ning, and Dan Roth. 2019 · 2019
Later among the works it cites.
Inducing relational knowledge from bert
Zied Bouraoui, José Camacho-Collados, and S. Schockaert. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
Shortcut learning in deep neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Who did what: A large-scale person-centered cloze dataset
Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester. 2016 · 2016
Cited alongside, same era.
Dailydialog: A manually labelled multi-turn dialogue dataset
Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017 · 2017
Cited alongside, same era.
Iso-timeml and the annotation of temporal information
James Pustejovsky. 2017 · 2017
Cited alongside, same era.
Wizard of wikipedia: Knowledge-powered conversational agents
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2018 · 2018
Cited alongside, same era.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A Smith. 2018 · 2018
Cited alongside, same era.
Temporal information extraction by predicting relative time-lines
Artuur Leeuwenberg and Marie Francine Moens. 2018 · 2018
Cited alongside, same era.
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 2020 · 2020
Later among the works it cites.
Injecting numerical reasoning skills into language models
Mor Geva, Ankit Gupta, and Jonathan Berant. 2020 · 2020
Later among the works it cites.
Adversarial filters of dataset biases
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew Peters, Ashish Sabharwal, and Yejin Choi. 2020 · 2020
Later among the works it cites.
Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models
Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren. 2020 · 2020
Later among the works it cites.
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions
Qiang Ning, Hao Wu, Rujun Han, Nanyun Peng, Matt Gardner, and Dan Roth. 2020 · 2020
Later among the works it cites.
Back to the future: Backpropagation-based decoding for unsupervised counterfactual and abductive reasoning
Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena D Hwang, Ronan Le Bras, Antoine Bosselut, and Yejin Choi. 2020 · 2020
Later among the works it 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. 2020 · 2020
Later among the works it cites.
Temporal reasoning in natural language inference
Siddharth Vashishtha, Adam Poliak, Yash Kumar Lal, Benjamin Van Durme, and Aaron Steven White. 2020 · 2020
Later among the works it cites.
Do language embeddings capture scales?
Xikun Zhang, Deepak Ramachandran, Ian Tenney, Yanai Elazar, and Dan Roth. 2020 · 2020
Later among the works it cites.
Temporal common sense acquisition with minimal supervision
Ben Zhou, Qiang Ning, Daniel Khashabi, and Dan Roth. 2020 · 2020
Later among the works it cites.