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20142024
most citedSequence to Sequence Learning with Neural Networks

13.4k citations · 28.2k across the 14 of their papers we have counts for

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5 papers · 1 filter

cs.CL2024238 cited

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Thomas Mesnard, Cassidy Hardin +105

This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate stro…

cs.CL20165.7k cited

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Yonghui Wu, Mike Schuster, Zhifeng Chen +28

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based tr…

cs.CL2014404 cited

Grammar as a Foreign Language

Oriol Vinyals, Lukasz Kaiser, Terry Koo +3

Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the…

cs.CL2014107 cited

Addressing the Rare Word Problem in Neural Machine Translation

Minh-Thang Luong, Ilya Sutskever, Quoc V. Le +2

Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in…

cs.CL201413.4k cited

Sequence to Sequence Learning with Neural Networks

Ilya Sutskever, Oriol Vinyals, Quoc V. Le

Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets a…