139 citations · 373 across the 15 of their papers we have counts for
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cs.CL2019★ 139 cited
What do you learn from context? Probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen +8
Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downst…
cs.CL2019★ 50 cited
BERT Rediscovers the Classical NLP Pipeline
Ian Tenney, Dipanjan Das, Ellie Pavlick
Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captur…
cs.CL2019★ 3 cited
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
Najoung Kim, Roma Patel, Adam Poliak +9
We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to t…