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Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events.
Roberta: A robustly optimized bert pretraining approach
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Semantic Representations for Question-Answering Systems
Martha Walton Evens. 1975 · 1975
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A preferential, pattern-seeking, semantics for natural language inference
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Resolving pronoun references
Jerry R. Hobbs. 1978 · 1978
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Automatic processing of large corpora for the resolution of anaphora references
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Selection and Information: A Class-Based Approach to Lexical Relationships
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George A. Miller. 1995 · 1995
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Selectional constraints: an information-theoretic model and its computational realization
P. Resnik. 1996 · 1996
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Selectional preference and sense disambiguation
Philip Resnik. 1997 · 1997
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Generalizing case frames using a thesaurus and the MDL principle
Hang Li and Naoki Abe. 1998 · 1998
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Class-based probability estimation using a semantic hierarchy
Stephen Clark and David Weir. 2002 · 2002
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Automatic labeling of semantic roles
Daniel Gildea and Daniel Jurafsky. 2002 · 2002
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On the role of conceptualization in commonsense knowledge graph construction
Mutian He, Y. Song, Kun Xu, and Y. Dong. 2020 · 2003
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Disambiguating nouns, verbs, and adjectives using automatically acquired selectional preferences
Diana McCarthy and John Carroll. 2003 · 2003
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Discovering asymmetric entailment relations between verbs using selectional preferences
Fabio Massimo Zanzotto, Marco Pennacchiotti, and Maria Teresa Pazienza. 2006 · 2006
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ISP: Learning inferential selectional preferences
Patrick Pantel, Rahul Bhagat, Bonaventura Coppola, Timothy Chklovski, and Eduard Hovy. 2007 · 2007
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Deriving generalized knowledge from corpora using WordNet abstraction
Benjamin Van Durme, Phillip Michalak, and Lenhart Schubert. 2009 · 2009
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A flexible, corpus-driven model of regular and inverse selectional preferences
Katrin Erk, Sebastian Padó, and Ulrike Padó. 2010 · 2010
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Latent variable models of selectional preference
Diarmuid Ó Séaghdha. 2010 · 2010
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Semeval-2012 task 7: Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Andrew S. Gordon, Zornitsa Kozareva, and Melissa Roemmele. 2011 · 2012
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Modelling selectional preferences in a lexical hierarchy
Diarmuid Ó Séaghdha and Anna Korhonen. 2012 · 2012
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Probase: A probabilistic taxonomy for text understanding
Wentao Wu, Hongsong Li, Haixun Wang, and Kenny Q. Zhu. 2012 · 2012
A surprisingly robust trick for the Winograd schema challenge
Vid Kocijan, Ana-Maria Cretu, Oana-Maria Camburu, Yordan Yordanov, and Thomas Lukasiewicz. 2019 · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
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Can a gorilla ride a camel? learning semantic plausibility from text
Ian Porada, Kaheer Suleman, and Jackie Chi Kit Cheung. 2019 · 2019
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Can neural networks understand monotonicity reasoning?
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki, Kentaro Inui, Satoshi Sekine, Lasha Abzianidze, and Johan Bos. 2019 · 2019
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Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen tau Yih, and Yejin Choi. 2020 · 2020
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Reporting bias and knowledge acquisition
Jonathan Gordon and Benjamin Van Durme. 2013 · 2013
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Selectional preferences for semantic role classification
Beñat Zapirain, Eneko Agirre, Lluís Màrquez, and Mihai Surdeanu. 2013 · 2013
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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A neural network approach to selectional preference acquisition
Tim Van de Cruys. 2014 · 2014
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Do supervised distributional methods really learn lexical inference relations?
Omer Levy, Steffen Remus, Chris Biemann, and Ido Dagan. 2015 · 2015
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Representing verbs as argument concepts
Yu Gong, Kaiqi Zhao, and Kenny Q. Zhu. 2016 · 2016
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Atticus Geiger, Kyle Richardson, and Christopher Potts. 2020 · 2020
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Comet-atomic 2020: On symbolic and neural commonsense knowledge graphs
Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, and Yejin Choi. 2020 · 2020
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K-bert: Enabling language representation with knowledge graph
Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. 2020 · 2020
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On the systematicity of probing contextualized word representations: The case of hypernymy in BERT
Abhilasha Ravichander, Eduard Hovy, Kaheer Suleman, Adam Trischler, and Jackie Chi Kit Cheung. 2020 · 2020
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Thinking like a skeptic: Defeasible inference in natural language
Rachel Rudinger, Vered Shwartz, Jena D. Hwang, Chandra Bhagavatula, Maxwell Forbes, Ronan Le Bras, Noah A. Smith, and Yejin Choi. 2020 · 2020
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Do neural language models overcome reporting bias?
Vered Shwartz and Yejin Choi. 2020 · 2020
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Probing neural language models for human tacit assumptions
Nathaniel Weir, Adam Poliak, and Benjamin Van Durme. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Julien Chaumond, Lysandre Debut, Victor Sanh, Clement Delangue, Anthony Moi, Pierric Cistac, Morgan Funtowicz, Joe Davison, Sam Shleifer, Remi Louf, Patrick von Platen, Tim Rault, Yacine Jernite, Teven Le Scao, Sylvain Gugger, Julien Plu, Clara Ma, Canwei Shen, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Adapting BERT for word sense disambiguation with gloss selection objective and example sentences
Boon Peng Yap, Andrew Koh, and Eng Siong Chng. 2020 · 2020
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Aser: A large-scale eventuality knowledge graph
Hongming Zhang, Xin Liu, Haojie Pan, Yangqiu Song, and Cane Wing-Ki Leung. 2020b · 2020
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Ontology-aware token embeddings for prepositional phrase attachment
Pradeep Dasigi, Waleed Ammar, Chris Dyer, and Eduard Hovy. 2017 · 2098
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