Gradient Salience Model

The Gradient Salience Model (GSM) is a computational framework used primarily in the context of natural language processing (NLP) to understand and generate attention mechanisms in neural networks, particularly in models dealing with tasks like sentiment analysis, machine translation, and textual entailment. This model emphasizes the importance of the distribution and strength of particular features (e.g., words, phrases) in the input data as they relate to the output predictions made by a neural network.