What Do You Learn from Context? Probing for Sentence Structure in Contextualized Word Representations
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.
Cite
Text
Tenney et al. "What Do You Learn from Context? Probing for Sentence Structure in Contextualized Word Representations." International Conference on Learning Representations, 2019.Markdown
[Tenney et al. "What Do You Learn from Context? Probing for Sentence Structure in Contextualized Word Representations." International Conference on Learning Representations, 2019.](https://mlanthology.org/iclr/2019/tenney2019iclr-you/)BibTeX
@inproceedings{tenney2019iclr-you,
title = {{What Do You Learn from Context? Probing for Sentence Structure in Contextualized Word Representations}},
author = {Tenney, Ian and Xia, Patrick and Chen, Berlin and Wang, Alex and Poliak, Adam and McCoy, R Thomas and Kim, Najoung and Van Durme, Benjamin and Bowman, Samuel R. and Das, Dipanjan and Pavlick, Ellie},
booktitle = {International Conference on Learning Representations},
year = {2019},
url = {https://mlanthology.org/iclr/2019/tenney2019iclr-you/}
}