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, Ellie Pavlick

Sep 27, 2018 ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: Contextualized representation models such as CoVe (McCann et al., 2017) and ELMo (Peters et al., 2018a) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from three recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that ELMo encodes linguistic structure at the word level better than other comparable models, and that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer small improvements on semantic tasks over a noncontextual baseline.
  • Keywords: natural language processing, word embeddings, transfer learning, interpretability
  • TL;DR: We probe for sentence structure in ELMo and related contextual embedding models. We find existing models efficiently encode syntax and show evidence of long-range dependencies, but only offer small improvements on semantic tasks.
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