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Conditional Variational Autoencoder for Neural Machine Translation
[article]
2018
arXiv
pre-print
We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. We extend this model with a co-attention mechanism motivated by Parikh et al. in the inference network. Compared to the vision domain, latent variable models for text face additional challenges due to
arXiv:1812.04405v1
fatcat:ct27stge5vhi3h7outuxhwx75u