Attention Is All You Need

Literature review

Attention Is All You Need

Attention Is All You Need is a paper by Vaswani et al. (2023). It is a seminal paper that introduced the transformer architecture, which has since become the backbone of many state-of-the-art models in natural language processing (NLP). The transformer architecture is known for its parallelism, scalability, and ability to capture long-range dependencies in sequences. The key innovation in the transformer is the self-attention mechanism, which allows the model to weigh the importance of different parts of the input sequence when making predictions. This mechanism has proven to be highly effective in capturing complex patterns in language data and has led to significant improvements in a wide range of NLP tasks.

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 –(Vaswani et al. 2023)

Outline

The Paper

paper

References

Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2023. “Attention Is All You Need.” https://arxiv.org/abs/1706.03762.