13.4k citations · 61k across the 117 of their papers we have counts for
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Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)
Chen Liang, Jonathan Berant, Quoc Le +2
Extending the success of deep neural networks to natural language understanding and symbolic reasoning requires complex operations and external memory. Recent neural program induct…
Learning a Natural Language Interface with Neural Programmer
Arvind Neelakantan, Quoc V. Le, Martin Abadi +2
Learning a natural language interface for database tables is a challenging task that involves deep language understanding and multi-step reasoning. The task is often approached by…
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Melvin Johnson, Mike Schuster, Quoc V. Le +9
We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no change in the model architectu…
Unsupervised Pretraining for Sequence to Sequence Learning
Prajit Ramachandran, Peter J. Liu, Quoc V. Le
This work presents a general unsupervised learning method to improve the accuracy of sequence to sequence (seq2seq) models. In our method, the weights of the encoder and decoder of…
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
Chen Liang, Jonathan Berant, Quoc Le +2
Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations…
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…