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Although models using contextual word embeddings have achieved state-of-the-art results on a host of NLP tasks, little is known about exactly what information these embeddings encode about the context words that they are understood to reflect.
Assessing BERT’s syntactic abilities
Yoav Goldberg. 2019 · 1901
Earlier work this paper cites.
GLoVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2016 · 2016
Earlier work this paper cites.
Probing for semantic evidence of composition by means of simple classification tasks
Allyson Ettinger, Ahmed Elgohary, and Philip Resnik. 2016 · 2016
Earlier work this paper cites.
Assessing the ability of LSTMs to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017 · 2017
Earlier work this paper cites.
RNN simulations of grammaticality judgments on long-distance dependencies
Shammur Absar Chowdhury and Roberto Zamparelli. 2018 · 2018
Earlier work this paper cites.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
Earlier work this paper cites.
Assessing composition in sentence vector representations
Allyson Ettinger, Ahmed Elgohary, Colin Phillips, and Philip Resnik. 2018 · 2018
Cited alongside, same era.
Colorless green recurrent networks dream hierarchically
Kristina Gulordava, Piotr Bojanowski, Edouard Grave, Tal Linzen, and Marco Baroni. 2018 · 2018
Cited alongside, same era.
Targeted syntactic evaluation of language models
Rebecca Marvin and Tal Linzen. 2018 · 2018
Cited alongside, same era.
Dissecting contextual word embeddings: Architecture and representation
Matthew Peters, Mark Neumann, Luke Zettlemoyer, and Wen-tau Yih. 2018a · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Cited alongside, same era.
What do RNN language models learn about filler–gap dependencies?
Ethan Wilcox, Roger Levy, Takashi Morita, and Richard Futrell. 2018 · 2018
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.
Neural language models as psycholinguistic subjects: Representations of syntactic state
Richard Futrell, Ethan Wilcox, Takashi Morita, Peng Qian, Miguel Ballesteros, and Roger Levy. 2019 · 2019
Later among the works it cites.
A structural probe for finding syntax in word representations
John Hewitt and Christopher D Manning. 2019 · 2019
Later among the works it cites.
What does BERT learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019 · 2019
Later among the works it cites.
Linguistic knowledge and transferability of contextual representations
Nelson F Liu, Matt Gardner, Yonatan Belinkov, Matthew E Peters, and Noah A Smith. 2019 · 2019
Later among the works it cites.
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Cited alongside, same era.
Language modeling teaches you more than translation does: Lessons learned through auxiliary syntactic task analysis
Kelly Zhang and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
Transformer-XL: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019 · 2019
Cited alongside, same era.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018b
Cited in the paper.
BERT rediscovers the classical NLP pipeline
Ian Tenney, Dipanjan Das, and Ellie Pavlick. 2019a
Cited in the paper.
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, et al. 2019b
Cited in the paper.
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019 · 2019
Later among the works it cites.
Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman. 2019 · 2019
Later among the works it cites.
What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models
Allyson Ettinger. 2020 · 2020
Closest in time.