Variational Neural Machine Translation with Normalizing Flows

Variational Neural Machine Translation (VNMT) is an attractive framework for modeling the generation of target translations, conditioned not only on the source sentence but also on some latent random variables. The latent variable modeling may introduce useful statistical dependencies that can improve translation accuracy... (read more)

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Methods used in the Paper


METHOD TYPE
Residual Connection
Skip Connections
Label Smoothing
Regularization
Multi-Head Attention
Attention Modules
Adam
Stochastic Optimization
ReLU
Activation Functions
Dropout
Regularization
BPE
Subword Segmentation
Dense Connections
Feedforward Networks
Layer Normalization
Normalization
Softmax
Output Functions
Scaled Dot-Product Attention
Attention Mechanisms
Transformer
Transformers