This paper proposes a new model, called condition-transforming variational
autoencoder (CTVAE), to improve the performance of conversation response
generation using conditional variational autoencoders (CVAEs). In conventional
CVAEs , the prior distribution of latent variable z follows a multivariate
Gaussian distribution with mean and variance modulated by the input conditions...
Previous work found that this distribution tends to become condition
independent in practical application. In our proposed CTVAE model, the latent
variable z is sampled by performing a non-lineartransformation on the
combination of the input conditions and the samples from a
condition-independent prior distribution N (0; I). In our objective
evaluations, the CTVAE model outperforms the CVAE model on fluency metrics and
surpasses a sequence-to-sequence (Seq2Seq) model on diversity metrics. In
subjective preference tests, our proposed CTVAE model performs significantly
better than CVAE and Seq2Seq models on generating fluency, informative and
topic relevant responses.