Generating Dialogue Responses from a Semantic Latent Space
Wei-Jen Ko, Avik Ray, Yilin Shen, Hongxia Jin · 2020
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.