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1905.06316
Cited By
What do you learn from context? Probing for sentence structure in contextualized word representations
International Conference on Learning Representations (ICLR), 2019
15 May 2019
Ian Tenney
Patrick Xia
Berlin Chen
Alex Jinpeng Wang
Adam Poliak
R. Thomas McCoy
Najoung Kim
Benjamin Van Durme
Samuel R. Bowman
Dipanjan Das
Ellie Pavlick
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Papers citing
"What do you learn from context? Probing for sentence structure in contextualized word representations"
50 / 555 papers shown
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How much pretraining data do language models need to learn syntax?
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282
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187
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267
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Coreference-Aware Dialogue Summarization
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BERT Embeddings for Automatic Readability Assessment
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Pre-Trained Models: Past, Present and Future
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Ning Ding
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AI4MH
386
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Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models
Annual Meeting of the Association for Computational Linguistics (ACL), 2021
Matthew Finlayson
Aaron Mueller
Sebastian Gehrmann
Stuart M. Shieber
Tal Linzen
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367
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BERTnesia: Investigating the capture and forgetting of knowledge in BERT
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Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge
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Yunfang Wu
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102
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The Low-Dimensional Linear Geometry of Contextualized Word Representations
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244
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How Reliable are Model Diagnostics?
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155
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Assessing the Syntactic Capabilities of Transformer-based Multilingual Language Models
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108
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Bird's Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach
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Mrinmaya Sachan
259
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248
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179
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260
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Effect of Post-processing on Contextualized Word Representations
International Conference on Computational Linguistics (COLING), 2021
Hassan Sajjad
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173
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Martin Wattenberg
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222
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