ELMo vs supervised.co

Embeddings from Language Models (ELMo) is a groundbreaking language representation model that helps machines understand complex characteristics of word usage, including both syntax and semantics, and recognizes the variations of word use across different linguistic contexts. ELMo achieves this by utilizing word vectors that are learned as functions of the internal states of a pre-trained deep bidirectional language model (biLM). BiLM uniquely models both forward and backward language model likelihoods, and when adding ELMo to a task-specific model, the weights of the biLM are frozen. The ELMo vector is then concatenated with a baseline representation of tokens and fed into a task RNN, enhancing the model's performance by providing rich, context-aware word embeddings.

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