What is recorded here?
Used internal states from a pretrained bidirectional language model as token-context representations, learning task-specific layer mixtures to augment downstream NLP models.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits LSTMs and earlier contextual language/translation representations. Distinctive deep layer mixing is not first invention of context-dependent embeddings or modern masked Transformer pretraining. Original experiments freeze pretrained LM weights and train task modules; source benchmark gains are not universal transfer guarantees.
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People and systems
Matthew E. Peters; Mark Neumann; Mohit Iyyer; Matt Gardner; Christopher Clark; Kenton Lee; Luke Zettlemoyer
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
ELMo: task-specific mixtures of deep contextual language-model states
Used internal states from a pretrained bidirectional language model as token-context representations, learning task-specific layer mixtures to augment downstream NLP models.
Scientific scope
Inherits LSTMs and earlier contextual language/translation representations. Distinctive deep layer mixing is not first invention of context-dependent embeddings or modern masked Transformer pretraining. Original experiments freeze pretrained LM weights and train task modules; source benchmark gains are not universal transfer guarantees.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Matthew E. Peters; Mark Neumann; Mohit Iyyer; Matt Gardner; Christopher Clark; Kenton Lee; Luke Zettlemoyer
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.