Semantic Scholar Open Access 2018 12091 sitasi

Deep Contextualized Word Representations

Matthew E. Peters Mark Neumann Mohit Iyyer Matt Gardner Christopher Clark +2 lainnya

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Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (7)

Matthew E. Peters

Mark Neumann

Mohit Iyyer

Matt Gardner

Christopher Clark

Kenton Lee

Luke Zettlemoyer

Format Sitasi

APA MLA BibTeX

Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K. et al. (2018). Deep Contextualized Word Representations. https://doi.org/10.18653/v1/N18-1202

Akses Cepat

Lihat di Sumber doi.org/10.18653/v1/N18-1202

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 12091×
Sumber Database: Semantic Scholar
DOI: 10.18653/v1/N18-1202
Akses: Open Access ✓