Attention Is All You Need

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. On the WMT 2014 English-to-French translation task, our model establishes a new single-model advanced 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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 The best performing models also connect the encoder and decoder through an attention mechanism.
• 2 We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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