  Existing open-domain dialogue generation models are usually trained to mimic
the gold response in the training set using cross-entropy loss on the
vocabulary. However, a good response does not need to resemble the gold
response, since there are multiple possible responses to a given prompt. In
this work, we hypothesize that the current models are unable to integrate
information from multiple semantically similar valid responses of a prompt,
resulting in the generation of generic and uninformative responses. To address
this issue, we propose an alternative to the end-to-end classification on
vocabulary. We learn the pair relationship between the prompts and responses as
a regression task on a latent space instead. In our novel dialog generation
model, the representations of semantically related sentences are close to each
other on the latent space. Human evaluation showed that learning the task on a
continuous space can generate responses that are both relevant and informative.
