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A lively research field has recently emerged that uses experimental methods to probe the linguistic behavior of modern deep networks.
Assessing BERT’s syntactic abilities
Yoav Goldberg · 1901
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Language and Mind
Noam Chomsky · 1968
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Knowledge of Language: Its Nature, Origin, and Use
Noam Chomsky · 1986
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Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations
David Rumelhart, James McClelland, and PDP Research Group, editors · 1986
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A Neurocomputational Perspective: The Nature of Mind and the Structure of Science
Paul Churchland · 1989
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Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing
Andy Clark · 1989
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Networks and theories: The place of connectionism in cognitive science
Michael McCloskey · 1991
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Semantics in Generative Grammar
Irene Heim and Angelika Kratzer · 1998
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Title Adverbs and Functional Heads: A Cross-Linguistic Perspective
Guglielmo Cinque · 1999
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The faculty of language: What is it, who has it, and how did it evolve?
Marc Hauser, Noam Chomsky, and Tecumseh Fitch · 2002
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Syntactic Theory: A Formal Introduction
Ivan Sag, Thomas Wasow, and Emily Bender · 2003
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Optimality Theory
Alan Prince and Paul Smolensky · 2004
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Constructions at Work: The Nature of Generalization in Language
Adele Goldberg · 2005
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A Study of Notions of Participation and Discourse in Argument Structure Realisation
Brian Murphy · 2007
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Speech and Language Processing, 2nd ed
Dan Jurafsky and James Martin · 2008
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The Syntax of Adjectives
Guglielmo Cinque · 2010
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A convolutional neural network for modelling sentences
Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc Le · 2014
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Structures, not strings: Linguistics as part of the cognitive sciences
Martin Everaert, Marinus Huybregts, Noam Chomsky, Robert Berwick, and Johan Bolhuis · 2015
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Affixation in semantic space: Modeling morpheme meanings with compositional distributional semantics
Marco Marelli and Marco Baroni · 2015
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Why Only Us: Language and Evolution
Robert Berwick and Noam Chomsky · 2016
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Assessing the ability of LSTMs to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg · 2016
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Fantastic DNimals and where to find them
Steven Scholte · 2016
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Using deep neural networks to learn syntactic agreement
Jean-Philippe Bernardy and Shalom Lappin · 2017
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Neural Network Methods for Natural Language Processing
Yoav Goldberg · 2017
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What sort of cognitive hypothesis is a derivational theory of grammar?
Tim Hunter · 2019
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The emergence of number and syntax units in LSTM language models
Yair Lakretz, Germán Kruszewski, Theo Desbordes, Dieuwke Hupkes, Stanislas Dehaene, and Marco Baroni · 2019
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RNNs implicitly implement tensor-product representations
Thomas McCoy, Tal Linzen, Ewan Dunbar, and Paul Smolensky · 2019
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Grammar and the use of data
Jon Sprouse and Carson Schütze · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel Bowman · 2019
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Grammaticality, acceptability, and probability: A probabilistic view of linguistic knowledge
Jey Han Lau, Alexander Clark, and Shalom Lappin · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Spicy adjectives and nominal donkeys: Capturing semantic deviance using compositionality in distributional spaces
Eva Maria Vecchi, Marco Marelli, Roberto Zamparelli, and Marco Baroni · 2017
Cited alongside, same era.
RNN simulations of grammaticality judgments on long-distance dependencies
Shammur Chowdhury and Roberto Zamparelli · 2018
Cited alongside, same era.
SentEval: An evaluation toolkit for universal sentence representations
Alexis Conneau and Douwe Kiela · 2018
Cited alongside, same era.
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, Germán Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni · 2018
Cited alongside, same era.
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Distributional semantics and linguistic theory
Gemma Boleda · 2020
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What don’t RNN language models learn about filler-gap dependencies?
Rui Chaves · 2020
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Transformers are RNNs: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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Grammatical Theory: From Transformational Grammar to Constraint-Based Approaches, 4th ed
Stefan Müller · 2020
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Grammaticality and language modelling
Jingcheng Niu and Gerald Penn · 2020
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Learning music helps you read: Using transfer to study linguistic structure in language models
Isabel Papadimitriou and Dan Jurafsky · 2020
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Explainable deep learning: A field guide for the uninitiated
Ning Xie, Gabrielle Ras, Marcel van Gerven, and Derek Doran · 2020
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Who speaks for us? Lessons from the Pinker letter
Itamar Kastner, Hadas Kotek, Anonymous Anonymous, Rikker Dockum, Michael Dow, Maria Esipova, Caitlin Green, and Todd Snider · 2021
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English speakers can infer Pokémon types using sound symbolism
Shigeto Kawahara, Gakuji Kumagai, and Mahayana Godoy · 2021
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What they do when in doubt: A study of inductive biases in seq2seq learners
Eugene Kharitonov and Rahma Chaabouni · 2021
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Mechanisms for handling nested dependencies in neural-network language models and humans
Yair Lakretz, Dieuwke Hupkes, Alessandra Vergallito, Marco Marelli, Marco Baroni, and Stanislas Dehaene · 2021
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Deep learning and linguistic representation
Shalom Lappin · 2021
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Syntactic structure from Deep Learning
Tal Linzen and Marco Baroni · 2021
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Locating and editing factual knowledge in GPT
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
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