In this paper, we propose a SPACEFUSION model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequenceto-sequence model and that of an autoencoder model by leveraging novel regularization terms.
our approach induces a latent space in which the distance and direction from the predicted response vector roughly match the relevance and diversity, respectively.