Before it
Sequence models were recurrent, and recurrence forced sequential computation.
The problem
Training could not be parallelised across positions, so it was slow.
What it introduced
Replace recurrence with stacked convolutions plus attention at every layer.
Why it matters
The road not taken. It showed removing recurrence was possible months before the Transformer did it differently.
What modern models inherit
Mostly historical: it marks that parallelism, not attention alone, was the prize.
What came next
The Transformer cited it and then overtook it comprehensively.