See how this paper only really has 2-3 load-bearing claims. Everything else is downstream. This demo shows that we understand foundations better than citation counts do.
These are the load-bearing claims that structure the paper's argument:
The Transformer model, based solely on attention mechanisms, outperforms existing models in machine translation tasks without using recurrence or convolutions.
The Transformer achieves state-of-the-art BLEU scores on the WMT 2014 English-to-German and English-to-French translation tasks, with significantly reduced training time and computational cost.
Self-attention allows for more parallelization and shorter path lengths between dependencies, improving the learning of long-range dependencies compared to recurrent and convolutional models.
The Transformer generalizes well to other tasks, such as English constituency parsing, demonstrating its versatility beyond translation tasks.
Multi-head attention enables the model to attend to information from different representation subspaces, enhancing its ability to capture complex dependencies.
The claim that self-attention alone can replace recurrence and convolution in sequence transduction models may be challenged due to existing reliance on these architectures in the literature.