We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

Anthology ID:

N18-1202

Volume:

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Month:

June

Year:

2018

Address:

New Orleans, Louisiana

Venue:

NAACL

SIG:

Publisher:

Association for Computational Linguistics

Note:

Pages:

2227–2237

Language:

URL:

https://aclanthology.org/N18-1202

DOI:

10.18653/v1/N18-1202

Bibkey:

Cite (ACL):

Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep Contextualized Word Representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2227–2237, New Orleans, Louisiana. Association for Computational Linguistics.

Cite (Informal):

Deep Contextualized Word Representations (Peters et al., NAACL 2018)

Copy Citation:

PDF:

https://aclanthology.org/N18-1202.pdf

Note:

 N18-1202.Notes.pdf

Video:

 http://vimeo.com/277672840

Code

 additional community code

Data

ACL ARC, CoNLL++, CoNLL-2003, OntoNotes 5.0, Penn Treebank, Reddit Corpus, SNLI, SQuAD, SST, Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison