                                            Provided proper attribution is provided, Google hereby grants permission to
                                            reproduce the tables and figures in this paper solely for use in journalistic or
                                                                           scholarly works.



                                                                     Attention Is All You Need
arXiv:1706.03762v7 [cs.CL] 2 Aug 2023




                                             Ashish Vaswani∗                Noam Shazeer∗               Niki Parmar∗             Jakob Uszkoreit∗
                                              Google Brain                   Google Brain             Google Research            Google Research
                                          avaswani@google.com             noam@google.com            nikip@google.com            usz@google.com

                                                Llion Jones∗                   Aidan N. Gomez∗ †                       Łukasz Kaiser∗
                                              Google Research                 University of Toronto                     Google Brain
                                             llion@google.com               aidan@cs.toronto.edu                 lukaszkaiser@google.com

                                                                                   Illia Polosukhin∗ ‡
                                                                             illia.polosukhin@gmail.com



                                                                                          Abstract

                                                  The dominant sequence transduction models are based on complex recurrent or
                                                  convolutional neural networks that include an encoder and a decoder. The best
                                                  performing models also connect the encoder and decoder through an attention
                                                  mechanism. We propose a new simple network architecture, the Transformer,
                                                  based solely on attention mechanisms, dispensing with recurrence and convolutions
                                                  entirely. Experiments on two machine translation tasks show these models to
                                                  be superior in quality while being more parallelizable and requiring significantly
                                                  less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-
                                                  to-German translation task, improving over the existing best results, including
                                                  ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task,
                                                  our model establishes a new single-model state-of-the-art BLEU score of 41.8 after
                                                  training for 3.5 days on eight GPUs, a small fraction of the training costs of the
                                                  best models from the literature. We show that the Transformer generalizes well to
                                                  other tasks by applying it successfully to English constituency parsing both with
                                                  large and limited training data.

                                            ∗
                                              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.
                                            †
                                              Work performed while at Google Brain.
                                            ‡
                                              Work performed while at Google Research.


                                        31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.
