An attention plot visualizes activations linking elements of an input sequence to elements of an output sequence. It turns a model assumption into a visible training diagnostic. section 9.2.2
Checking alignment in voice synthesis
A banking application team builds a voice assistant and assumes characters will align linearly with audio over time. The plot places audio frames on the x-axis and characters on the y-axis. A nearly diagonal pattern supports the assumption; substantial deviation signals a problem. Guided attention addresses this by penalizing departures from the diagonal with an auxiliary loss. This differs from machine translation, where attention resolves alignments across languages with different syntax or token ordering. section 9.2.2
Figure 9.12 Attention plot evolution through training without (left) and with (right) guided attention loss
Compare attention-map evolution during training with and without guided attention loss. section 9.2.2
