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Analogies play a central role in human commonsense reasoning.
Assessing bert’s syntactic abilities
Yoav Goldberg. 2019 · 1901
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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.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2019 · 1910
Earlier work this paper cites.
Connectionist models and their properties
Jerome A. Feldman and Dana H. Ballard. 1982 · 1982
Earlier work this paper cites.
Neural networks and physical systems with emergent collective computational abilities
John J. Hopfield. 1982 · 1982
Earlier work this paper cites.
Learning distributed representations of concepts
Geoffrey E. Hinton. 1986 · 1986
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Distributed representations
Geoffrey E. Hinton, James L. McClelland, and David E. Rumelhart. 1986 · 1986
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Arguing by analogy in law: A case-based model
Kevin D Ashley. 1988 · 1988
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Word association norms, mutual information, and lexicography
Kenneth Church and Patrick Hanks. 1990 · 1990
Earlier work this paper cites.
Mental leaps: Analogy in creative thought
Keith J Holyoak, Keith James Holyoak, and Paul Thagard. 1996 · 1996
Earlier work this paper cites.
A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge
Thomas K. Landauer and Susan T. Dumais. 1997 · 1997
Earlier work this paper cites.
Combining independent modules in lexical multiple-choice problems
Peter D. Turney, Michael L. Littman, Jeffrey Bigham, and Victor Shnayder. 2003 · 2003
Earlier work this paper cites.
Measuring semantic similarity by latent relational analysis
Peter D. Turney. 2005 · 2005
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Analogical dissimilarity: definition, algorithms and two experiments in machine learning
Laurent Miclet, Sabri Bayoudh, and Arnaud Delhay. 2008 · 2008
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Similarity, precedent and argument from analogy
Douglas Walton. 2010 · 2010
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2020 · 2012
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Linguistic regularities in sparse and explicit word representations
Omer Levy and Yoav Goldberg. 2014 · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014 · 2014
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Solving and explaining analogy questions using semantic networks
Adrian Boteanu and Sonia Chernova. 2015 · 2015
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
On using monolingual corpora in neural machine translation
Caglar Gulcehre, Orhan Firat, Kelvin Xu, Kyunghyun Cho, Loic Barrault, Huei-Chi Lin, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
The role of analogy in ontology alignment: A study on lisa
Elie Raad and Joerg Evermann. 2015 · 2015
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A latent variable model approach to pmi-based word embeddings
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski. 2016 · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
Cited alongside, same era.
Listen, attend and spell: A neural network for large vocabulary conversational speech recognition
William Chan, Navdeep Jaitly, Quoc Le, and Oriol Vinyals. 2016 · 2016
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Word embeddings, analogies, and machine learning: Beyond king-man+ woman= queen
Aleksandr Drozd, Anna Gladkova, and Satoshi Matsuoka. 2016 · 2016
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Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn’t
Anna Gladkova, Aleksandr Drozd, and Satoshi Matsuoka. 2016 · 2016
Cited alongside, same era.
Analogical classifiers: a theoretical perspective
Nicolas Hug, Henri Prade, Gilles Richard, and Mathieu Serrurier. 2016 · 2016
Cited alongside, same era.
Towards understanding linear word analogies
Kawin Ethayarajh, David Duvenaud, and Graeme Hirst. 2019 · 2019
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What bert is not: Lessons from a new suite of psycholinguistic diagnostics for language models
Allyson Ettinger. 2019 · 2019
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Computational design, analogy, and creativity
Ashok Goel. 2019 · 2019
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Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them
Hila Gonen and Yoav Goldberg. 2019 · 2019
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A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning. 2019 · 2019
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What does BERT learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019 · 2019
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Issues in evaluating semantic spaces using word analogies
Tal Linzen. 2016 · 2016
Cited alongside, same era.
When the whole is less than the sum of its parts: How composition affects pmi values in distributional semantic vectors
Denis Paperno and Marco Baroni. 2016 · 2016
Cited alongside, same era.
Take and took, gaggle and goose, book and read: Evaluating the utility of vector differences for lexical relation learning
Ekaterina Vylomova, Laura Rimell, Trevor Cohn, and Timothy Baldwin. 2016 · 2016
Cited alongside, same era.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
Skip-gram- zipf+ uniform= vector additivity
Alex Gittens, Dimitris Achlioptas, and Michael W Mahoney. 2017 · 2017
Cited alongside, same era.
Compositional approaches for representing relations between words: A comparative study
Huda Hakami and Danushka Bollegala. 2017 · 2017
Cited alongside, same era.
Accelerating innovation through analogy mining
Tom Hope, Joel Chan, Aniket Kittur, and Dafna Shahaf. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Understanding learning dynamics of language models with SVCCA
Naomi Saphra and Adam Lopez. 2019 · 2019
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Quantity doesn’t buy quality syntax with neural language models
Marten van Schijndel, Aaron Mueller, and Tal Linzen. 2019 · 2019
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BERT has a mouth, and it must speak: BERT as a Markov random field language model
Alex Wang and Kyunghyun Cho. 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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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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Understanding the source of semantic regularities in word embeddings
Hsiao-Yu Chiang, Jose Camacho-Collados, and Zachary Pardos. 2020 · 2020
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Analogies minus analogy test: measuring regularities in word embeddings
Louis Fournier, Emmanuel Dupoux, and Ewan Dunbar. 2020 · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 2020 · 2020
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Towards analogy-based explanations in machine learning
Eyke Hüllermeier. 2020 · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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How can we accelerate progress towards human-like linguistic generalization?
Tal Linzen. 2020 · 2020
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Fair is better than sensational: Man is to doctor as woman is to doctor
Malvina Nissim, Rik van Noord, and Rob van der Goot. 2020 · 2020
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A university map of course knowledge
Zachary A. Pardos and Andrew J. H. Nam. 2020 · 2020
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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A primer in bertology: What we know about how bert works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2021 · 2021
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