Attention mechanisms in NLP are techniques that enable models to dynamically focus on specific parts of input data when generating output. They were introduced to address limitations in earlier sequence-to-sequence (seq2seq) models, which struggled to handle long input sequences. In traditional seq2seq architectures, the encoder compressed the entire input into a fixed-length vector, often losing important details. Attention mechanisms solve this by allowing the decoder to reference the encoder’s hidden states selectively, prioritizing the most relevant information at each step. For example, in machine translation, when converting the French sentence “Le chat est assis sur le tapis” to English, the model might focus on “chat” and “assis” to produce “The cat sat” while paying less attention to other words.
The core idea involves calculating “attention scores” between the decoder’s current state and all encoder states. These scores determine how much weight each encoder state should receive when constructing the decoder’s output. A common approach uses dot-product attention: for each decoder step, the model computes similarity scores between the decoder’s hidden state (query) and the encoder’s hidden states (keys). These scores are normalized via softmax to create a probability distribution (attention weights), which is then used to create a weighted sum of the encoder states (values). For instance, when translating “She loves reading books” to German, generating the word “liest” (reads) might involve higher weights on “loves” and “reading” in the input. This process allows the model to adaptively prioritize input tokens based on context.
Beyond basic attention, variations like self-attention and multi-head attention have become foundational in modern architectures like Transformers. Self-attention lets input tokens interact with each other, capturing relationships within a sequence (e.g., connecting “it” to “book” in “The book is long, but it’s engaging”). Multi-head attention runs multiple attention operations in parallel, enabling the model to learn diverse patterns. These mechanisms power models like BERT and GPT, where they improve tasks such as text summarization (focusing on key sentences) or question answering (aligning questions to relevant passage segments). By replacing recurrent layers with attention, models also gain computational efficiency, as they process sequences in parallel rather than step-by-step. This flexibility and performance explain why attention is now a standard tool in NLP.