Tensor2Tensor for Neural Machine Translation

Abstract

Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.

arXiv commentarXiv admin note: text overlap with arXiv:1706.03762

References in corpus (11)

• Neural Machine Translation by Jointly Learning to Align and Translate
• Sequence to Sequence Learning with Neural Networks
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• WaveNet: A Generative Model for Raw Audio
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Convolutional Sequence to Sequence Learning
• Xception: Deep Learning with Depthwise Separable Convolutions
• Neural Machine Translation in Linear Time
• Depthwise Separable Convolutions for Neural Machine Translation
• The Reversible Residual Network: Backpropagation Without Storing Activations
• Encoding Source Language with Convolutional Neural Network for Machine Translation

Cited by in corpus (2)

• CrossedWires: A Dataset of Syntactically Equivalent but Semantically Disparate Deep Learning Models
• Neural Duplicate Question Detection without Labeled Training Data