Understanding LSTM, ELMO, and Transformer Models for Natural Language Processing

LSTM Model

The LSTM (Long Short‑Term Memory) network addresses the long‑distance dependency problem of standard RNNs by introducing three gates—forget, input, and output—that regulate the flow of information through a cell state, allowing selective memory retention and forgetting.

The forget gate uses a sigmoid function to decide which parts of the previous cell state to discard, the input gate determines new candidate values to add, and the output gate combines the updated cell state with a sigmoid‑controlled filter and a tanh activation to produce the final output.

ELMO Model

ELMO (Embeddings from Language Models) tackles word‑sense ambiguity by first pre‑training a two‑layer bidirectional LSTM on large corpora, then extracting contextualized word embeddings from each layer for downstream tasks.

During pre‑training, the model predicts each word from its surrounding context (both left and right), producing three representations for each token: the original word embedding, the first‑layer bidirectional LSTM output, and the second‑layer bidirectional LSTM output, which are later combined with learned weights for tasks such as question answering.

Transformer Model

The Transformer architecture, built on self‑attention mechanisms, supersedes recurrent models by processing all tokens in parallel and capturing long‑range dependencies without recurrence.

While this article does not detail the full Transformer design, it points readers to additional resources for a deeper dive into its components and applications.