ELMo

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.

Key Features

• Deep Contextualized Word Representations: ELMo provides word vectors that deeply understand the context and nuances of word usage.

• Pre-trained Bidirectional Language Model: Utilizes internal states from a biLM pre-trained on extensive text corpuses for more accurate embeddings.

• Enhancement of Task-specific Models: ELMo vectors can be added to existing models to improve their performance by providing contextual information.

• Modeling of Polysemy: Recognizes and represents the multiple meanings of a word depending on its linguistic context.

• Syntax and Semantics Modeling: Captures complex characteristics of language use, such as sentence structure and meaning.

FAQ

What is ELMo?

ELMo stands for Embeddings from Language Models and it is a type of language representation model that provides deep contextualized word representations.

What does ELMo model?

ELMo models both the complexity of word use and how word usage changes across different linguistic contexts, also known as polysemy.

How does ELMo generate word vectors?

ELMo uses word vectors that are functions of the internal states of a deep bidirectional language model, which has been pre-trained on a large text corpus.

How can ELMo be added to a supervised model?

To add ELMo to a task-specific model, you freeze the pre-trained biLM's weights, concatenate the ELMo vector with the token representation, and pass this enhanced representation into the task RNN.

What is a deep bidirectional language model (biLM)?

A deep bidirectional language model (biLM) trains simultaneously on text sequences forward and backward, resulting in a richer, more complex understanding of language structure.