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/Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057)
/Publisher (Curran Associates\054 Inc\056)
/Language (en\055US)
/Created (2017)
/EventType (Poster)
/Description-Abstract (The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration\056 The best performing such models also connect the encoder and decoder through an attentionm echanisms\056 We propose a novel\054 simple network architecture based solely onan attention mechanism\054 dispensing with recurrence and convolutions entirely\056Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train\056 Our single model with 165 million parameters\054 achieves 27\0565 BLEU onEnglish\055to\055German translation\054 improving over the existing best ensemble result by over 1 BLEU\056 On English\055to\055French translation\054 we outperform the previoussingle state\055of\055the\055art with model by 0\0567 BLEU\054 achieving a BLEU score of 41\0561\056)
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/Title (Attention is All you Need)
/Date (2017)
/ModDate (D\07220180212212210\05508\04700\047)
/Published (2017)
/Type (Conference Proceedings)
/firstpage (5998)
/Book (Advances in Neural Information Processing Systems 30)
/Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051)
/Editors (I\056 Guyon and U\056V\056 Luxburg and S\056 Bengio and H\056 Wallach and R\056 Fergus and S\056 Vishwanathan and R\056 Garnett)
/Author (Ashish Vaswani\054 Noam Shazeer\054 Niki Parmar\054 Jakob Uszkoreit\054 Llion Jones\054 Aidan N\056 Gomez\054 \225ukasz Kaiser\054 Illia Polosukhin)
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