What do you learn from context? Probing for sentence structure in contextualized word representations. Tenney, I., Xia, P., Chen, B., Wang, A., Poliak, A., McCoy, R T., Kim, N., Van Durme, B., Bowman, S. R, Das, D., & Pavlick, E. In International Conference of Learning Representations (ICLR 2019), pages 1–17, 2019. ![What do you learn from context? Probing for sentence structure in contextualized word representations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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.
@inproceedings{Tenney2019,
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.},
archivePrefix = {arXiv},
arxivId = {1905.06316},
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 of Learning Representations (ICLR 2019)},
eprint = {1905.06316},
file = {:Users/shanest/Documents/Library/Tenney et al/International Conference of Learning Representations (ICLR 2019)/Tenney et al. - 2019 - What do you learn from context Probing for sentence structure in contextualized word representations.pdf:pdf},
keywords = {method: diagnostic classifier,method: model comparison,phenomenon: various},
pages = {1--17},
title = {{What do you learn from context? Probing for sentence structure in contextualized word representations}},
url = {http://arxiv.org/abs/1905.06316},
year = {2019}
}
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