attentionweighting
Attention weighting is a computational technique used in machine learning and artificial intelligence to emphasize certain parts of input data while downplaying others. It is commonly applied in natural language processing (NLP) tasks, such as machine translation, text summarization, and question answering, where not all words or phrases contribute equally to the task at hand. By dynamically adjusting the importance of different input elements, attention mechanisms help models focus on relevant information, improving performance and interpretability.
The concept was first introduced in the context of neural machine translation by Bahdanau et al. in
In practice, attention weights are typically computed using learned parameters and activation functions, such as softmax,
Beyond NLP, attention weighting has been applied in computer vision, reinforcement learning, and other domains where