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arXiv:1802.05365cs.CL

Deep contextualized word representations

Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer

Abstract

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.

arXiv comments
NAACL 2018. Originally posted to openreview 27 Oct 2017. v2 updated for NAACL camera ready
Journal reference
DOI
Subjects
cs.CLComputation and Language

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Linked by 4 identifiable commenters in 4 comments across 4 threads, from Sep 2018 to Sep 2023.

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Discussed in comments4 threads

  1. ERNIE, China's ChatGPT, cracks under pressure 1 comment

    • There was actually a series of language models named after Sesame Street characters back in 2018-2020, starting with ELMo, then BERT, ERNIE (a different model from 2019), Big Bird, ... There are likely some more that I missed. ELMo: https://arxiv.org/abs/1802.05365 BERT…

      NaiveBayesianView on HNParent context

  2. Ask HN: What were the papers on the list Ilya Sutskever gave John Carmack? 1 comment

    • I posted a list of papers on twitter, and will be posting a summary for each of them as well. here is the list https://twitter.com/vrungta/status/1623343807227105280 Unlocking the Secrets of AI: A Journey through the Foundational Papers by @vrungta (2023) 1. "Attention is All…

      vikashrungtaView on HNParent context

  3. Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative 1 comment

    • These guys have done what looks like an impressive amount of work... but I was disappointed to see there's no mention of generative language modeling at all and only a very brief mention of key advances in image generation over the past few years. Examples of generative language…

      cs702View on HNParent context

  4. Making a racist AI without really trying (2017) 1 comment

    • There's a long list of papers on that - 'multi-sense word embeddings'. But more recently we have found that passing the raw character embeddings through a two layer BiLSTM will resolve the ambiguity of meaning from context - 'ElMO'. https://arxiv.org/abs/1802.05365 (state of the…

      visargaView on HNParent context

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