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Contextualized representation models such as ELMo (Peters et al., 2018a) andBERT (Devlin et al., 2018) have recently achieved state-of-the-art results on adiverse array of downstream NLP tasks. Building on recent token-level probingwork, we introduce a novel edge probing task design and construct a broad suiteof sub-sentence tasks derived from the traditional structured NLP pipeline. Weprobe word-level contextual representations from four recent models andinvestigate how they encode sentence structure across a range of syntactic,semantic, local, and long-range phenomena. We find that existing models trainedon language modeling and translation produce strong representations forsyntactic phenomena, but only offer comparably small improvements on semantictasks over a non-contextual baseline.

What do you learn from context? Probing for sentence structure in contextualized word representations