ELMO representations are deep, contextual and character based NLP

(written by lawrence krubner, however indented passages are often quotes). You can contact lawrence at: lawrence@krubner.com


ELMo is a 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). These 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. They can be easily added to existing models and significantly improve the state of the art across a broad range of challenging NLP problems, including question answering, textual entailment and sentiment analysis.

ELMo representations are:

Contextual: The representation for each word depends on the entire context in which it is used.

Deep: The word representations combine all layers of a deep pre-trained neural network.

Character based: ELMo representations are purely character based, allowing the network to use morphological clues to form robust representations for out-of-vocabulary tokens unseen in training.