The Attention Mechanism from Scratch

The attention mechanism was introduced to improve the performance of the encoder-decoder model for machine translation. The idea behind the attention mechanism was to permit the decoder to utilize the most relevant parts of the input sequence in a flexible manner, by a weighted combination of all of the encoded input vectors, with the most relevant vectors being attributed the highest weights. In this tutorial, you will discover the attention mechanism and its implementation. The Attention Mechanism from Scratch Photo by Nitish Meena, some rights reserved. The attention mechanism was introduced by Bahdanau et al. (2014), to address the bottleneck problem that arises with the use of a fixed-length encoding vector, where the decoder would have limited access to the information provided by the input. This is thought to become especially problematic for long and/or complex sequences, where the dimensionality of their representation would be forced to be the same as for shorter or simpler sequences.