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Analysis methods which enable us to better understand the representations and functioning of neural models of language are increasingly needed as deep learning becomes the dominant approach in NLP.
Finding structure in time
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Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
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Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
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Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
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Danilo Croce, Simone Filice, Giuseppe Castellucci, and Roberto Basili. 2017 · 2017
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Discourse-based objectives for fast unsupervised sentence representation learning
Yacine Jernite, Samuel R Bowman, and David Sontag. 2017 · 2017
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Visualisation and ‘diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure
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An efficient framework for learning sentence representations
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Limitations in learning an interpreted language with recurrent models
Denis Paperno. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Evaluating the ability of lstms to learn context-free grammars
Luzi Sennhauser and Robert Berwick. 2018 · 2018
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Closing brackets with recurrent neural networks
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Attention is all you need
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Alexis Conneau, Germán Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
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