  The generation of personalized dialogue is vital to natural and human-like
conversation. Typically, personalized dialogue generation models involve
conditioning the generated response on the dialogue history and a
representation of the persona/personality of the interlocutor. As it is
impractical to obtain the persona/personality representations for every
interlocutor, recent works have explored the possibility of generating
personalized dialogue by finetuning the model with dialogue examples
corresponding to a given persona instead. However, in real-world
implementations, a sufficient number of corresponding dialogue examples are
also rarely available. Hence, in this paper, we propose a Dual Latent Variable
Generator (DLVGen) capable of generating personalized dialogue in the absence
of any persona/personality information or any corresponding dialogue examples.
Unlike prior work, DLVGen models the latent distribution over potential
responses as well as the latent distribution over the agent's potential
persona. During inference, latent variables are sampled from both distributions
and fed into the decoder. Empirical results show that DLVGen is capable of
generating diverse responses which accurately incorporate the agent's persona.
