Contextual Embeddings
Introduction
Unlike static word embeddings (Word2Vec, GloVe), contextual embeddings generate different representations for the same word based on its surrounding context. This revolutionary approach powers modern NLP models like BERT and GPT.
Static vs Contextual Embeddings
Static embeddings assign the same vector regardless of context, while contextual embeddings adapt based on surrounding words.
Interactive Contextual Embedding Visualizer
Token Embeddings
Heatmap showing embedding values for each token. Blue = positive, Red = negative.
Self-Attention Weights
Attention weights showing how much the target word attends to other words.
Evolution of Contextual Embeddings
ELMo (2018)
- • Architecture: Bidirectional LSTMs
- • Key Innovation: Character-level CNN + bi-LSTM layers
- • Context: Combines forward and backward language models
- • Usage: Feature extraction, concatenated with task-specific models
BERT (2018)
- • Architecture: Transformer encoder (self-attention)
- • Key Innovation: Masked language modeling + next sentence prediction
- • Context: Bidirectional attention over entire sequence
- • Usage: Fine-tuning entire model for downstream tasks
GPT Series (2018-2023)
- • Architecture: Transformer decoder (causal attention)
- • Key Innovation: Autoregressive language modeling at scale
- • Context: Left-to-right attention only
- • Usage: Few-shot learning, prompt engineering
How Contextual Embeddings Work
Position-aware Processing
Unlike static embeddings, contextual models process the entire sequence:
- Tokenize input text
- Add positional encodings/embeddings
- Process through multiple layers
- Each layer refines representations based on context
Attention Mechanisms
Self-attention allows each token to "look at" all other tokens:
Attention(Q,K,V) = softmax(QK^T/√d)VThis creates context-aware representations at each position.
Applications and Impact
Disambiguation
Correctly understanding polysemous words (bank, bat, bear) based on context.
Transfer Learning
Pre-trained models fine-tuned for specific tasks with minimal data.
Few-shot Learning
Models like GPT can adapt to new tasks with just a few examples.
Key Takeaways
- Contextual embeddings revolutionized NLP by solving the polysemy problem
- Each word gets a unique embedding based on its specific context
- ELMo used LSTMs, while BERT and GPT use transformer architectures
- Self-attention mechanisms are key to capturing long-range dependencies
- Pre-training on large corpora enables effective transfer learning
- These embeddings form the foundation of modern LLMs and NLP systems