The architecture was Shazeer's, but the rough idea came from Jakob Uszkoreit who initiated the project.

Uszkoreit wanted to build a more efficient/scalable language/seq2seq model that could take advantage of GPU parallelism (replacing RNNs which were the main approach to sequence modelling at that time).

Uszkoreit's insight was that although language appears sequential, it is in fact really part parallel part hierarchical, as can be seen by linguist's sentence parse trees where at each level there is parallelism/independence between the branches of the tree, with them getting combined at the next level up. This is what gave rise to the idea of a model that consisted of a stack of of parallel processing layers (transformer layers). I believe that attention was also part of the plan from day one, as this had already been proven to be valuable (Bahdanau) with RNN seq2seq modelling.

So, this is what Uszkoreit wanted to build, but by his own account he failed to come up with an implementation that matched or outperformed the prevailing RNN approach that he wanted to replace. At this point, Uszkoreit mentioned the idea to Shazeer, who got on board and eventually arrived at a performant architecture which was then pared back by an ablation process resulting in the initial encoder-decoder Transformer architecture. Shazeer later came up with the mixture-of-experts architecture, and also other optimizations after he left to found character.ai

Curious about others' contributions, such as Vaswani, Parmar, Jones and Gomez, to the paper. What sucks about co-authorship in research papers is that you don't get a clean breakdown of who contributed what to the research paper, and the distribution (in more cases than not) is very much like a pareto distribution.

I'm talking from plenty of group project experience here.

Can you expound on the ablation process? Is that referring to a stripping down of the data or weights or something? Or a stripping down of the transformer architecture structurally? Just curious