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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields. Expand
Bidirectional recurrent neural networks
It is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. Expand
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
GNMT, Google's Neural Machine Translation system, is presented, which attempts to address many of the weaknesses of conventional phrase-based translation systems and provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delicited models. Expand
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that mapsExpand
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
This work proposes a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages using a shared wordpiece vocabulary, and introduces an artificial token at the beginning of the input sentence to specify the required target language. Expand
One billion word benchmark for measuring progress in statistical language modeling
We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quicklyExpand
Exploring the Limits of Language Modeling
This work explores recent advances in Recurrent Neural Networks for large scale Language Modeling, and extends current models to deal with two key challenges present in this task: corpora and vocabulary sizes, and complex, long term structure of language. Expand
Statistical parametric speech synthesis using deep neural networks
This paper examines an alternative scheme that is based on a deep neural network (DNN), the relationship between input texts and their acoustic realizations is modeled by a DNN, and experimental results show that the DNN- based systems outperformed the HMM-based systems with similar numbers of parameters. Expand
Reward Augmented Maximum Likelihood for Neural Structured Prediction
This paper presents a simple and computationally efficient approach to incorporate task reward into a maximum likelihood framework, and shows that an optimal regularized expected reward is achieved when the conditional distribution of the outputs given the inputs is proportional to their exponentiated scaled rewards. Expand
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
This paper identifies several key modeling and training techniques and applies them to the RNN architecture, yielding a new RNMT+ model that outperforms all of the three fundamental architectures on the benchmark WMT'14 English to French and English to German tasks. Expand