![[1706.03762] Attention Is All You Need . Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin. Google Research | Bonnes pratiques en documentation | Scoop.it](https://img.scoop.it/R3Nf-aV-zFCPDr3s1jV_-zl72eJkfbmt4t8yenImKBVvK0kTmF0xjctABnaLJIm9)
From
arxiv
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. 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.
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L'article scientifique « L'attention est tout ce dont vous avez besoin » a présenté le Transformer, une nouvelle architecture de réseau neuronal qui facilite la compréhension du langage. Avant le Transformer, les machines n’étaient pas très douées pour comprendre des phrases longues car elles étaient incapables de distinguer les relations entre des mots éloignés les uns des autres. Le Transformer a changé la donne, devenant la pierre angulaire des systèmes de compréhension du langage et d’IA générative les plus impressionnants aujourd’hui. Traduction, synthèse de texte, réponses aux questions et même génération d’images et robotique : le Transformer a révolutionné la manière dont les machines effectuent toutes ces actions.