Jointly Optimizing Diversity and Relevance in Neural Response Generation

Understand

Although recent neural conversation models have shown great potential, they often generate bland and generic responses.

• While various approaches have been explored to diversify the output of the conversation model, the improvement often comes at the cost of decreased relevance.
• In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms.
• As a result, our approach induces a latent space in which the distance and direction from the predicted response vector roughly match the relevance and diversity, respectively.