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      "statement": "# ELMo: task-specific mixtures of deep contextual language-model states\n\nUsed internal states from a pretrained bidirectional language model as token-context representations, learning task-specific layer mixtures to augment downstream NLP models.\n\n## Scientific scope\n\nInherits 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.\n\n## Reading status\n\nThis 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.\n\n## People and systems\n\nMatthew E. Peters; Mark Neumann; Mohit Iyyer; Matt Gardner; Christopher Clark; Kenton Lee; Luke Zettlemoyer\n\nSource-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.\n\n\n\n## Original sources\n\n- [Deep contextualized word representations](https://arxiv.org/abs/1802.05365v1)\n",
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