This understates his criticality. The author list was randomized, but the critical idea was truly his. Wonder what this says about GDM …
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
The research history leading up to this was interesting - there had been a bunch of work, in various domains, on "autoencoder" architectures used to learn compact representations for things like dimensionality reduction and sequence representation. The idea was to have an encoder-decoder pair, connected by a limited bottleneck representation, with the training goal of the decoder reconstructing the encoder input from the bottleneck representation.
One example of this was to learn a fixed size(!) sequence (e.g. sentence) representation using an LSTM-based autoencoder (LSTM->embedding->LSTM), which at the time seemed rather shocking - the ability to represent a variable length sequence with a fixed size embedding. Equally shocking was that you could use this for machine translation simply by connecting an LSTM encoder for one language to an LSTM decoder for another language.
This type of LSTM->LSTM seq2seq encode-decode architecture for machine translation was then improved by Bahdanau by replacing the fixed size representation with an attention mechanism so the decoder could learn to be more specific about input-output relationships.
This type of LSTM-based seq2seq encode-decode architecture, using attention, is what Uszkoreit et al set out to improve - to make more efficient by using a parallel vs sequential (RNN) architecture. The Transformer was never conceived of as purely for language modelling, or as an "AI" architecture. Later when the usage focused on language modelling (generation, not translation), the encoder was dropped since input and output are the same thing.
I'm talking from plenty of group project experience here.
Why? If you read a research paper for its content this is not especially important.
This thread is more about the people of course, and here we care, but that's not the point of a research paper.
Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.