Attention Is All You Need
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 Englishto-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 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.
The paper presents a new architecture for encoder/decoder models for sequence-to-sequence modeling that is solely based on (multi-layered) attention networks combined with standard Feed-Forward networks as opposed to the common scheme of using recurrent or convolutional neural networks. The paper presents two main advantages of this new architecture: (1) Reduced training time due to reduced complexity of the architecture, and (2) new State-of-the-Art result on standard WMT data sets, outperforming previous work by about 1 BLEU point.
Strengths:
- The paper argues well that (1) can be achieved by avoiding recurrent or convolutional layers and the complexity analysis in Table 1 strengthens the argument.
- (2) is shown by comparing the model performance against strong baselines on two language pairs, English-German and English-French.
The main strengths of the paper are that it proposes an entirely novel architecture without recurrence or convolutions, and advances state of the art.
Weaknesses:
- While the general architecture of the model is described well and is illustrated by figures, architectural details lack mathematical definition, for example multi-head attention. Why is there a split arrow in Figure 2 right, bottom right? I assume these are the inputs for the attention layer, namely query, keys, and values. Are the same vectors used for keys and values here or different sections of them? A formal definition of this would greatly help readers understand this.
- The proposed model contains lots of hyperparameters, and the most important ones are evaluated in ablation studies in the experimental section. It would have been nice to see significance tests for the various configurations in Table 3.
- The complexity argument claims that self-attention models have a maximum path length of 1 which should help maintaining information flow between distant symbols (i.e. long-range dependencies). It would be good to see this empirically validated by evaluating performance on long sentences specifically.
Minor comments:
- Are you using dropout on the source/target embeddings?
- Line 146: There seems to be dangling "2"