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Recently, a variety of probing tasks are proposed to discover linguistic properties learned in contextualized word embeddings.
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Deep contextualized word representations
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Dissecting contextual word embeddings: Architecture and representation
Matthew Peters, Mark Neumann, Luke Zettlemoyer, and Wen-tau Yih · 2018
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Continuous hierarchical representations with poincaré variational auto-encoders
Emile Mathieu, Charline Le Lan, Chris J Maddison, Ryota Tomioka, and Yee Whye Teh · 2019
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Visualizing and measuring the geometry of bert
Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B Viegas, Andy Coenen, Adam Pearce, and Been Kim · 2019
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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, Sam Bowman, Dipanjan Das, and Ellie Pavlick · 2019
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Poincaré glove: Hyperbolic word embeddings
Alexandru Tifrea, Gary Becigneul, and Octavian-Eugen Ganea · 2019
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A tale of a probe and a parser
Rowan Hall Maudslay, Josef Valvoda, Tiago Pimentel, Adina Williams, and Ryan Cotterell · 2020
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Riemannian adaptive optimization methods
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning · 2019
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Are pre-trained language models aware of phrases? simple but strong baselines for grammar induction
Taeuk Kim, Jihun Choi, Daniel Edmiston, and Sang-goo Lee · 2020
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Probing natural language inference models through semantic fragments
Kyle Richardson, Hai Hu, Lawrence S Moss, and Ashish Sabharwal · 2020
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A primer in BERTology: What we know about how BERT works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky · 2020
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