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
Abstract
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 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 four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline.
BibTeX
@inproceedings{
tenney2018what,
title={What do you learn from context? Probing for sentence structure in contextualized word representations},
author={Ian Tenney and Patrick Xia and Berlin Chen and Alex Wang and Adam Poliak and R Thomas McCoy and Najoung Kim and Benjamin Van Durme and Sam Bowman and Dipanjan Das and Ellie Pavlick},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=SJzSgnRcKX},
}