Afterwards, we replace the line-by-line decision layer with the poem-level encoder and final decision layer and train towards the per-poem decisions based on the parameters estimated before.
the final model is able to steer its attention mechanism towards the important lines and can learn to sacrifice the initially trained per-line optimization for the overall per-poem optimization.