entAttention

entAttention is a mechanism used in deep learning models, particularly in natural language processing, to allow a model to focus on specific parts of the input data when processing it. It was introduced as a way to improve the performance of sequence-to-sequence models, such as those used for machine translation. The core idea behind entAttention is to assign different "attention weights" to different input elements, indicating their relative importance for the current task.

When a model processes a sequence, it often encounters situations where not all parts of the input are equally relevant for generating the output at a particular step. For example, when translating a sentence, a specific word in the output might heavily depend on a few words in the input, while other input words are less crucial. entAttention addresses this by dynamically calculating these weights. It essentially allows the model to "look back" at the input and decide where to place its "attention" at each step of generating the output.

The mechanism typically involves calculating a score between the current state of the decoder (which is generating the output) and each element of the input sequence. These scores are then converted into probabilities using a softmax function, resulting in the attention weights. These weights are used to compute a weighted sum of the input representations, creating a context vector that is then used by the decoder to generate the next element of the output. This selective focus helps models handle long sequences more effectively and can lead to more accurate and contextually relevant outputs.